Overview

Dataset statistics

Number of variables56
Number of observations1054
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory461.2 KiB
Average record size in memory448.1 B

Variable types

Numeric16
Categorical40

Alerts

PERIODO has constant value "2020.0" Constant
CLAVE has a high cardinality: 1054 distinct values High cardinality
SITIO has a high cardinality: 1052 distinct values High cardinality
MUNICIPIO has a high cardinality: 447 distinct values High cardinality
ACUIFERO has a high cardinality: 272 distinct values High cardinality
df_index is highly correlated with ORGANISMO_DE_CUENCA and 3 other fieldsHigh correlation
LONGITUD is highly correlated with df_index and 3 other fieldsHigh correlation
LATITUD is highly correlated with df_index and 3 other fieldsHigh correlation
ALC_mg/L is highly correlated with CALIDAD_ALC and 2 other fieldsHigh correlation
CONDUCT_mS/cm is highly correlated with CALIDAD_CONDUC and 9 other fieldsHigh correlation
SDT_M_mg/L is highly correlated with CONDUCT_mS/cm and 3 other fieldsHigh correlation
FLUORUROS_mg/L is highly correlated with ALC_mg/L and 1 other fieldsHigh correlation
DUR_mg/L is highly correlated with CONDUCT_mS/cm and 11 other fieldsHigh correlation
COLI_FEC_NMP/100_mL is highly correlated with CALIDAD_COLI_FEC and 1 other fieldsHigh correlation
N_NO3_mg/L is highly correlated with CONDUCT_mS/cm and 3 other fieldsHigh correlation
AS_TOT_mg/L is highly correlated with FLUORUROS_mg/L and 2 other fieldsHigh correlation
CR_TOT_mg/L is highly correlated with PERIODOHigh correlation
HG_TOT_mg/L is highly correlated with CALIDAD_HG and 3 other fieldsHigh correlation
PB_TOT_mg/L is highly correlated with CALIDAD_PB and 1 other fieldsHigh correlation
MN_TOT_mg/L is highly correlated with HG_TOT_mg/L and 4 other fieldsHigh correlation
FE_TOT_mg/L is highly correlated with HG_TOT_mg/L and 3 other fieldsHigh correlation
ORGANISMO_DE_CUENCA is highly correlated with df_index and 3 other fieldsHigh correlation
ESTADO is highly correlated with df_index and 14 other fieldsHigh correlation
SUBTIPO is highly correlated with ESTADOHigh correlation
PERIODO is highly correlated with CUMPLE_CON_DUR and 34 other fieldsHigh correlation
CALIDAD_ALC is highly correlated with ALC_mg/L and 2 other fieldsHigh correlation
CALIDAD_CONDUC is highly correlated with ESTADO and 7 other fieldsHigh correlation
CALIDAD_SDT_ra is highly correlated with ESTADO and 10 other fieldsHigh correlation
CALIDAD_SDT_salin is highly correlated with CONDUCT_mS/cm and 9 other fieldsHigh correlation
CALIDAD_FLUO is highly correlated with ESTADO and 2 other fieldsHigh correlation
CALIDAD_DUR is highly correlated with ESTADO and 9 other fieldsHigh correlation
CALIDAD_COLI_FEC is highly correlated with COLI_FEC_NMP/100_mL and 1 other fieldsHigh correlation
CALIDAD_N_NO3 is highly correlated with N_NO3_mg/L and 2 other fieldsHigh correlation
CALIDAD_AS is highly correlated with ESTADO and 3 other fieldsHigh correlation
CD_TOT_mg/L is highly correlated with CALIDAD_CD and 1 other fieldsHigh correlation
CALIDAD_CD is highly correlated with CD_TOT_mg/L and 1 other fieldsHigh correlation
CALIDAD_CR is highly correlated with ESTADO and 1 other fieldsHigh correlation
CALIDAD_HG is highly correlated with HG_TOT_mg/L and 3 other fieldsHigh correlation
CALIDAD_PB is highly correlated with PB_TOT_mg/L and 1 other fieldsHigh correlation
CALIDAD_MN is highly correlated with MN_TOT_mg/L and 2 other fieldsHigh correlation
CALIDAD_FE is highly correlated with CUMPLE_CON_FEHigh correlation
SEMAFORO is highly correlated with ESTADO and 4 other fieldsHigh correlation
CUMPLE_CON_ALC is highly correlated with ALC_mg/L and 1 other fieldsHigh correlation
CUMPLE_CON_COND is highly correlated with CONDUCT_mS/cm and 8 other fieldsHigh correlation
CUMPLE_CON_SDT_ra is highly correlated with CONDUCT_mS/cm and 8 other fieldsHigh correlation
CUMPLE_CON_SDT_salin is highly correlated with CONDUCT_mS/cm and 8 other fieldsHigh correlation
CUMPLE_CON_FLUO is highly correlated with ESTADO and 3 other fieldsHigh correlation
CUMPLE_CON_DUR is highly correlated with ESTADO and 8 other fieldsHigh correlation
CUMPLE_CON_CF is highly correlated with COLI_FEC_NMP/100_mL and 1 other fieldsHigh correlation
CUMPLE_CON_NO3 is highly correlated with N_NO3_mg/L and 1 other fieldsHigh correlation
CUMPLE_CON_AS is highly correlated with CALIDAD_FLUO and 3 other fieldsHigh correlation
CUMPLE_CON_CD is highly correlated with CD_TOT_mg/L and 1 other fieldsHigh correlation
CUMPLE_CON_CR is highly correlated with ESTADO and 1 other fieldsHigh correlation
CUMPLE_CON_HG is highly correlated with HG_TOT_mg/L and 3 other fieldsHigh correlation
CUMPLE_CON_PB is highly correlated with PB_TOT_mg/L and 1 other fieldsHigh correlation
CUMPLE_CON_MN is highly correlated with CALIDAD_MNHigh correlation
CUMPLE_CON_FE is highly correlated with CALIDAD_FEHigh correlation
SDT_M_mg/L is highly skewed (γ1 = 25.08739509) Skewed
CR_TOT_mg/L is highly skewed (γ1 = 31.54467997) Skewed
HG_TOT_mg/L is highly skewed (γ1 = 23.65435824) Skewed
FE_TOT_mg/L is highly skewed (γ1 = 31.13993832) Skewed
df_index is uniformly distributed Uniform
CLAVE is uniformly distributed Uniform
SITIO is uniformly distributed Uniform
df_index has unique values Unique
CLAVE has unique values Unique

Reproduction

Analysis started2022-11-15 22:20:51.859268
Analysis finished2022-11-15 22:21:23.024809
Duration31.17 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct1054
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean533.829222
Minimum0
Maximum1067
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2022-11-15T17:21:23.111562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile53.65
Q1266.25
median533.5
Q3800.75
95-th percentile1014.35
Maximum1067
Range1067
Interquartile range (IQR)534.5

Descriptive statistics

Standard deviation308.8293358
Coefficient of variation (CV)0.5785171045
Kurtosis-1.200905513
Mean533.829222
Median Absolute Deviation (MAD)267.5
Skewness3.48830345 × 10-5
Sum562656
Variance95375.55865
MonotonicityStrictly increasing
2022-11-15T17:21:23.184529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
7021
 
0.1%
7041
 
0.1%
7051
 
0.1%
7061
 
0.1%
7071
 
0.1%
7081
 
0.1%
7091
 
0.1%
7101
 
0.1%
7111
 
0.1%
Other values (1044)1044
99.1%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
ValueCountFrequency (%)
10671
0.1%
10661
0.1%
10651
0.1%
10641
0.1%
10631
0.1%
10621
0.1%
10611
0.1%
10601
0.1%
10591
0.1%
10581
0.1%

CLAVE
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct1054
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
DLAGU6
 
1
OCFSU2989
 
1
OCFSU2994
 
1
OCFSU3048
 
1
OCFSU3077
 
1
Other values (1049)
1049 

Length

Max length11
Median length9
Mean length8.938330171
Min length6

Characters and Unicode

Total characters9421
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1054 ?
Unique (%)100.0%

Sample

1st rowDLAGU6
2nd rowDLAGU6516
3rd rowDLAGU7
4th rowDLAGU9
5th rowDLBAJ107

Common Values

ValueCountFrequency (%)
DLAGU61
 
0.1%
OCFSU29891
 
0.1%
OCFSU29941
 
0.1%
OCFSU30481
 
0.1%
OCFSU30771
 
0.1%
OCFSU30971
 
0.1%
OCFSU31001
 
0.1%
OCFSU31071
 
0.1%
OCFSU31091
 
0.1%
OCFSU31121
 
0.1%
Other values (1044)1044
99.1%

Length

2022-11-15T17:21:23.352193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dlagu61
 
0.1%
dlbaj2021
 
0.1%
dlbaj1891
 
0.1%
dlagu71
 
0.1%
dlagu91
 
0.1%
dlbaj1071
 
0.1%
dlbaj1081
 
0.1%
dlbaj1101
 
0.1%
dlbaj1111
 
0.1%
dlbaj1171
 
0.1%
Other values (1044)1044
99.1%

Most occurring characters

ValueCountFrequency (%)
C823
 
8.7%
D733
 
7.8%
L709
 
7.5%
O662
 
7.0%
2531
 
5.6%
1531
 
5.6%
4492
 
5.2%
3465
 
4.9%
5435
 
4.6%
6424
 
4.5%
Other values (24)3616
38.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter5396
57.3%
Decimal Number4025
42.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C823
15.3%
D733
13.6%
L709
13.1%
O662
12.3%
A363
 
6.7%
U310
 
5.7%
N251
 
4.7%
P222
 
4.1%
R213
 
3.9%
M178
 
3.3%
Other values (14)932
17.3%
Decimal Number
ValueCountFrequency (%)
2531
13.2%
1531
13.2%
4492
12.2%
3465
11.6%
5435
10.8%
6424
10.5%
0297
7.4%
7292
7.3%
8285
7.1%
9273
6.8%

Most occurring scripts

ValueCountFrequency (%)
Latin5396
57.3%
Common4025
42.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
C823
15.3%
D733
13.6%
L709
13.1%
O662
12.3%
A363
 
6.7%
U310
 
5.7%
N251
 
4.7%
P222
 
4.1%
R213
 
3.9%
M178
 
3.3%
Other values (14)932
17.3%
Common
ValueCountFrequency (%)
2531
13.2%
1531
13.2%
4492
12.2%
3465
11.6%
5435
10.8%
6424
10.5%
0297
7.4%
7292
7.3%
8285
7.1%
9273
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII9421
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C823
 
8.7%
D733
 
7.8%
L709
 
7.5%
O662
 
7.0%
2531
 
5.6%
1531
 
5.6%
4492
 
5.2%
3465
 
4.9%
5435
 
4.6%
6424
 
4.5%
Other values (24)3616
38.4%

SITIO
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1052
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
POZO VILLA UNION
 
2
EL FUERTE
 
2
FINCA SANTA CRUZ
 
1
EST. JUAREZ
 
1
SANTA TERESA 2A. SECCION
 
1
Other values (1047)
1047 

Length

Max length95
Median length57
Mean length21.08823529
Min length4

Characters and Unicode

Total characters22227
Distinct characters55
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1050 ?
Unique (%)99.6%

Sample

1st rowPOZO SAN GIL
2nd rowPOZO R013 CAÑADA HONDA
3rd rowPOZO COSIO
4th rowPOZO EL SALITRILLO
5th rowRANCHO EL TECOLOTE

Common Values

ValueCountFrequency (%)
POZO VILLA UNION2
 
0.2%
EL FUERTE2
 
0.2%
FINCA SANTA CRUZ1
 
0.1%
EST. JUAREZ1
 
0.1%
SANTA TERESA 2A. SECCION1
 
0.1%
MACAYO 3RA. SECCION1
 
0.1%
RANCHO LOS TOROS1
 
0.1%
TACSA, RANCHO SAN ANTONIO1
 
0.1%
MIGUEL ALEMAN1
 
0.1%
POZO SAN GIL1
 
0.1%
Other values (1042)1042
98.9%

Length

2022-11-15T17:21:23.423234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pozo594
 
15.0%
de225
 
5.7%
san95
 
2.4%
el93
 
2.4%
189
 
2.3%
la82
 
2.1%
276
 
1.9%
no75
 
1.9%
del68
 
1.7%
agua57
 
1.4%
Other values (1385)2496
63.2%

Most occurring characters

ValueCountFrequency (%)
2903
13.1%
O2528
 
11.4%
A2460
 
11.1%
E1567
 
7.0%
L1084
 
4.9%
P1048
 
4.7%
N1015
 
4.6%
I1010
 
4.5%
S855
 
3.8%
R838
 
3.8%
Other values (45)6919
31.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter17781
80.0%
Space Separator2903
 
13.1%
Decimal Number811
 
3.6%
Other Punctuation291
 
1.3%
Dash Punctuation148
 
0.7%
Close Punctuation128
 
0.6%
Open Punctuation128
 
0.6%
Lowercase Letter31
 
0.1%
Connector Punctuation4
 
< 0.1%
Math Symbol1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O2528
14.2%
A2460
13.8%
E1567
 
8.8%
L1084
 
6.1%
P1048
 
5.9%
N1015
 
5.7%
I1010
 
5.7%
S855
 
4.8%
R838
 
4.7%
C815
 
4.6%
Other values (17)4561
25.7%
Decimal Number
ValueCountFrequency (%)
1223
27.5%
2156
19.2%
3102
12.6%
069
 
8.5%
464
 
7.9%
556
 
6.9%
742
 
5.2%
637
 
4.6%
833
 
4.1%
929
 
3.6%
Lowercase Letter
ValueCountFrequency (%)
o25
80.6%
a2
 
6.5%
d1
 
3.2%
i1
 
3.2%
p1
 
3.2%
s1
 
3.2%
Other Punctuation
ValueCountFrequency (%)
.251
86.3%
,29
 
10.0%
/5
 
1.7%
#4
 
1.4%
"2
 
0.7%
Space Separator
ValueCountFrequency (%)
2903
100.0%
Dash Punctuation
ValueCountFrequency (%)
-148
100.0%
Close Punctuation
ValueCountFrequency (%)
)128
100.0%
Open Punctuation
ValueCountFrequency (%)
(128
100.0%
Connector Punctuation
ValueCountFrequency (%)
_4
100.0%
Math Symbol
ValueCountFrequency (%)
+1
100.0%
Other Letter
ValueCountFrequency (%)
º1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17813
80.1%
Common4414
 
19.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
O2528
14.2%
A2460
13.8%
E1567
 
8.8%
L1084
 
6.1%
P1048
 
5.9%
N1015
 
5.7%
I1010
 
5.7%
S855
 
4.8%
R838
 
4.7%
C815
 
4.6%
Other values (24)4593
25.8%
Common
ValueCountFrequency (%)
2903
65.8%
.251
 
5.7%
1223
 
5.1%
2156
 
3.5%
-148
 
3.4%
)128
 
2.9%
(128
 
2.9%
3102
 
2.3%
069
 
1.6%
464
 
1.4%
Other values (11)242
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII22199
99.9%
None28
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2903
13.1%
O2528
 
11.4%
A2460
 
11.1%
E1567
 
7.1%
L1084
 
4.9%
P1048
 
4.7%
N1015
 
4.6%
I1010
 
4.5%
S855
 
3.9%
R838
 
3.8%
Other values (43)6891
31.0%
None
ValueCountFrequency (%)
Ñ27
96.4%
º1
 
3.6%

ORGANISMO_DE_CUENCA
Categorical

HIGH CORRELATION

Distinct13
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
CUENCAS CENTRALES DEL NORTE
231 
LERMA SANTIAGO PACIFICO
166 
PENINSULA DE YUCATAN
125 
NOROESTE
93 
PENINSULA DE BAJA CALIFORNIA
86 
Other values (8)
353 

Length

Max length28
Median length23
Mean length18.86907021
Min length6

Characters and Unicode

Total characters19888
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLERMA SANTIAGO PACIFICO
2nd rowLERMA SANTIAGO PACIFICO
3rd rowLERMA SANTIAGO PACIFICO
4th rowLERMA SANTIAGO PACIFICO
5th rowPENINSULA DE BAJA CALIFORNIA

Common Values

ValueCountFrequency (%)
CUENCAS CENTRALES DEL NORTE231
21.9%
LERMA SANTIAGO PACIFICO166
15.7%
PENINSULA DE YUCATAN125
11.9%
NOROESTE93
8.8%
PENINSULA DE BAJA CALIFORNIA86
 
8.2%
BALSAS68
 
6.5%
RIO BRAVO65
 
6.2%
PACIFICO NORTE60
 
5.7%
GOLFO NORTE53
 
5.0%
AGUAS DEL VALLE DE MEXICO37
 
3.5%
Other values (3)70
 
6.6%

Length

2022-11-15T17:21:23.490588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
norte344
11.5%
del268
 
9.0%
de248
 
8.3%
pacifico242
 
8.1%
cuencas231
 
7.7%
centrales231
 
7.7%
peninsula211
 
7.1%
lerma166
 
5.6%
santiago166
 
5.6%
yucatan125
 
4.2%
Other values (13)751
25.2%

Most occurring characters

ValueCountFrequency (%)
A2353
11.8%
E2244
11.3%
1929
9.7%
N1752
8.8%
C1445
 
7.3%
O1391
 
7.0%
R1188
 
6.0%
L1177
 
5.9%
S1155
 
5.8%
I1135
 
5.7%
Other values (12)4119
20.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter17959
90.3%
Space Separator1929
 
9.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A2353
13.1%
E2244
12.5%
N1752
9.8%
C1445
8.0%
O1391
7.7%
R1188
 
6.6%
L1177
 
6.6%
S1155
 
6.4%
I1135
 
6.3%
T1013
 
5.6%
Other values (11)3106
17.3%
Space Separator
ValueCountFrequency (%)
1929
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17959
90.3%
Common1929
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A2353
13.1%
E2244
12.5%
N1752
9.8%
C1445
8.0%
O1391
7.7%
R1188
 
6.6%
L1177
 
6.6%
S1155
 
6.4%
I1135
 
6.3%
T1013
 
5.6%
Other values (11)3106
17.3%
Common
ValueCountFrequency (%)
1929
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII19888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A2353
11.8%
E2244
11.3%
1929
9.7%
N1752
8.8%
C1445
 
7.3%
O1391
 
7.0%
R1188
 
6.0%
L1177
 
5.9%
S1155
 
5.8%
I1135
 
5.7%
Other values (12)4119
20.7%

ESTADO
Categorical

HIGH CORRELATION

Distinct32
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
DURANGO
119 
SONORA
102 
YUCATAN
85 
ZACATECAS
75 
COAHUILA DE ZARAGOZA
 
59
Other values (27)
614 

Length

Max length31
Median length20
Mean length10.04459203
Min length6

Characters and Unicode

Total characters10587
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGUASCALIENTES
2nd rowAGUASCALIENTES
3rd rowAGUASCALIENTES
4th rowAGUASCALIENTES
5th rowBAJA CALIFORNIA SUR

Common Values

ValueCountFrequency (%)
DURANGO119
 
11.3%
SONORA102
 
9.7%
YUCATAN85
 
8.1%
ZACATECAS75
 
7.1%
COAHUILA DE ZARAGOZA59
 
5.6%
BAJA CALIFORNIA SUR48
 
4.6%
SAN LUIS POTOSI47
 
4.5%
GUANAJUATO40
 
3.8%
HIDALGO37
 
3.5%
CHIHUAHUA35
 
3.3%
Other values (22)407
38.6%

Length

2022-11-15T17:21:23.549303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
durango119
 
7.6%
de116
 
7.4%
sonora102
 
6.5%
yucatan85
 
5.5%
baja77
 
4.9%
california77
 
4.9%
zacatecas75
 
4.8%
zaragoza59
 
3.8%
coahuila59
 
3.8%
sur48
 
3.1%
Other values (35)741
47.6%

Most occurring characters

ValueCountFrequency (%)
A2186
20.6%
O1024
 
9.7%
C738
 
7.0%
I633
 
6.0%
U626
 
5.9%
N624
 
5.9%
S531
 
5.0%
R504
 
4.8%
504
 
4.8%
L493
 
4.7%
Other values (15)2724
25.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter10083
95.2%
Space Separator504
 
4.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A2186
21.7%
O1024
10.2%
C738
 
7.3%
I633
 
6.3%
U626
 
6.2%
N624
 
6.2%
S531
 
5.3%
R504
 
5.0%
L493
 
4.9%
E417
 
4.1%
Other values (14)2307
22.9%
Space Separator
ValueCountFrequency (%)
504
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10083
95.2%
Common504
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A2186
21.7%
O1024
10.2%
C738
 
7.3%
I633
 
6.3%
U626
 
6.2%
N624
 
6.2%
S531
 
5.3%
R504
 
5.0%
L493
 
4.9%
E417
 
4.1%
Other values (14)2307
22.9%
Common
ValueCountFrequency (%)
504
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10587
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A2186
20.6%
O1024
 
9.7%
C738
 
7.0%
I633
 
6.0%
U626
 
5.9%
N624
 
5.9%
S531
 
5.0%
R504
 
4.8%
504
 
4.8%
L493
 
4.7%
Other values (15)2724
25.7%

MUNICIPIO
Categorical

HIGH CARDINALITY

Distinct447
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
LA PAZ
 
26
PARRAS
 
24
ENSENADA
 
24
HERMOSILLO
 
17
MERIDA
 
16
Other values (442)
947 

Length

Max length49
Median length35
Mean length10.22865275
Min length4

Characters and Unicode

Total characters10781
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique236 ?
Unique (%)22.4%

Sample

1st rowASIENTOS
2nd rowAGUASCALIENTES
3rd rowCOSIO
4th rowRINCON DE ROMOS
5th rowLA PAZ

Common Values

ValueCountFrequency (%)
LA PAZ26
 
2.5%
PARRAS24
 
2.3%
ENSENADA24
 
2.3%
HERMOSILLO17
 
1.6%
MERIDA16
 
1.5%
CUENCAME14
 
1.3%
SAN JUAN DEL RIO13
 
1.2%
TORREON11
 
1.0%
MULEGE11
 
1.0%
LERDO11
 
1.0%
Other values (437)887
84.2%

Length

2022-11-15T17:21:23.615480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de90
 
5.5%
san66
 
4.0%
la35
 
2.1%
rio32
 
2.0%
del28
 
1.7%
paz27
 
1.7%
parras24
 
1.5%
ensenada24
 
1.5%
villa22
 
1.3%
luis19
 
1.2%
Other values (491)1263
77.5%

Most occurring characters

ValueCountFrequency (%)
A1680
15.6%
E996
 
9.2%
O857
 
7.9%
L765
 
7.1%
N662
 
6.1%
I628
 
5.8%
C584
 
5.4%
576
 
5.3%
R562
 
5.2%
T499
 
4.6%
Other values (19)2972
27.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter10200
94.6%
Space Separator576
 
5.3%
Other Punctuation5
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A1680
16.5%
E996
 
9.8%
O857
 
8.4%
L765
 
7.5%
N662
 
6.5%
I628
 
6.2%
C584
 
5.7%
R562
 
5.5%
T499
 
4.9%
S476
 
4.7%
Other values (17)2491
24.4%
Space Separator
ValueCountFrequency (%)
576
100.0%
Other Punctuation
ValueCountFrequency (%)
.5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10200
94.6%
Common581
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A1680
16.5%
E996
 
9.8%
O857
 
8.4%
L765
 
7.5%
N662
 
6.5%
I628
 
6.2%
C584
 
5.7%
R562
 
5.5%
T499
 
4.9%
S476
 
4.7%
Other values (17)2491
24.4%
Common
ValueCountFrequency (%)
576
99.1%
.5
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII10753
99.7%
None28
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A1680
15.6%
E996
 
9.3%
O857
 
8.0%
L765
 
7.1%
N662
 
6.2%
I628
 
5.8%
C584
 
5.4%
576
 
5.4%
R562
 
5.2%
T499
 
4.6%
Other values (17)2944
27.4%
None
ValueCountFrequency (%)
Ñ22
78.6%
Ü6
 
21.4%

ACUIFERO
Categorical

HIGH CARDINALITY

Distinct272
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
PENINSULA DE YUCATAN
119 
PRINCIPAL-REGION LAGUNERA
 
27
ALTO ATOYAC
 
19
TEPEHUANES-SANTIAGO
 
16
LA PAILA
 
12
Other values (267)
861 

Length

Max length38
Median length26
Mean length15.42504744
Min length5

Characters and Unicode

Total characters16258
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)5.8%

Sample

1st rowVALLE DE CHICALOTE
2nd rowVALLE DE CHICALOTE
3rd rowVALLE DE AGUASCALIENTES
4th rowVALLE DE AGUASCALIENTES
5th rowTODOS SANTOS

Common Values

ValueCountFrequency (%)
PENINSULA DE YUCATAN119
 
11.3%
PRINCIPAL-REGION LAGUNERA27
 
2.6%
ALTO ATOYAC19
 
1.8%
TEPEHUANES-SANTIAGO16
 
1.5%
LA PAILA12
 
1.1%
GENERAL CEPEDA-SAUCEDA12
 
1.1%
MEOQUI-DELICIAS12
 
1.1%
VALLE DEL MEZQUITAL12
 
1.1%
LA PAZ11
 
1.0%
SAN LUIS POTOSI10
 
0.9%
Other values (262)804
76.3%

Length

2022-11-15T17:21:23.687156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de275
 
12.1%
valle119
 
5.3%
peninsula119
 
5.3%
yucatan119
 
5.3%
rio106
 
4.7%
del71
 
3.1%
san65
 
2.9%
la52
 
2.3%
lagunera27
 
1.2%
principal-region27
 
1.2%
Other values (331)1284
56.7%

Most occurring characters

ValueCountFrequency (%)
A2475
15.2%
E1542
 
9.5%
L1323
 
8.1%
1210
 
7.4%
N1051
 
6.5%
O1009
 
6.2%
I976
 
6.0%
C801
 
4.9%
U775
 
4.8%
R756
 
4.7%
Other values (18)4340
26.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter14760
90.8%
Space Separator1210
 
7.4%
Dash Punctuation278
 
1.7%
Other Punctuation10
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A2475
16.8%
E1542
10.4%
L1323
 
9.0%
N1051
 
7.1%
O1009
 
6.8%
I976
 
6.6%
C801
 
5.4%
U775
 
5.3%
R756
 
5.1%
T716
 
4.9%
Other values (15)3336
22.6%
Space Separator
ValueCountFrequency (%)
1210
100.0%
Dash Punctuation
ValueCountFrequency (%)
-278
100.0%
Other Punctuation
ValueCountFrequency (%)
.10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin14760
90.8%
Common1498
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A2475
16.8%
E1542
10.4%
L1323
 
9.0%
N1051
 
7.1%
O1009
 
6.8%
I976
 
6.6%
C801
 
5.4%
U775
 
5.3%
R756
 
5.1%
T716
 
4.9%
Other values (15)3336
22.6%
Common
ValueCountFrequency (%)
1210
80.8%
-278
 
18.6%
.10
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII16219
99.8%
None39
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A2475
15.3%
E1542
 
9.5%
L1323
 
8.2%
1210
 
7.5%
N1051
 
6.5%
O1009
 
6.2%
I976
 
6.0%
C801
 
4.9%
U775
 
4.8%
R756
 
4.7%
Other values (17)4301
26.5%
None
ValueCountFrequency (%)
Ñ39
100.0%

SUBTIPO
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
POZO
1025 
MANANTIAL
 
12
CENOTE
 
7
POZO NORIA
 
4
NORIA
 
3
Other values (3)
 
3

Length

Max length13
Median length4
Mean length4.108159393
Min length4

Characters and Unicode

Total characters4330
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.3%

Sample

1st rowPOZO
2nd rowPOZO
3rd rowPOZO
4th rowPOZO
5th rowPOZO

Common Values

ValueCountFrequency (%)
POZO1025
97.2%
MANANTIAL12
 
1.1%
CENOTE7
 
0.7%
POZO NORIA4
 
0.4%
NORIA3
 
0.3%
DESCARGA1
 
0.1%
Pozo1
 
0.1%
BOMBEO CENOTE1
 
0.1%

Length

2022-11-15T17:21:23.751111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:23.821053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
pozo1030
97.3%
manantial12
 
1.1%
cenote8
 
0.8%
noria7
 
0.7%
descarga1
 
0.1%
bombeo1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
O2075
47.9%
P1030
23.8%
Z1029
23.8%
A45
 
1.0%
N39
 
0.9%
T20
 
0.5%
I19
 
0.4%
E18
 
0.4%
M13
 
0.3%
L12
 
0.3%
Other values (9)30
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4322
99.8%
Space Separator5
 
0.1%
Lowercase Letter3
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O2075
48.0%
P1030
23.8%
Z1029
23.8%
A45
 
1.0%
N39
 
0.9%
T20
 
0.5%
I19
 
0.4%
E18
 
0.4%
M13
 
0.3%
L12
 
0.3%
Other values (6)22
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
o2
66.7%
z1
33.3%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4325
99.9%
Common5
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O2075
48.0%
P1030
23.8%
Z1029
23.8%
A45
 
1.0%
N39
 
0.9%
T20
 
0.5%
I19
 
0.4%
E18
 
0.4%
M13
 
0.3%
L12
 
0.3%
Other values (8)25
 
0.6%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O2075
47.9%
P1030
23.8%
Z1029
23.8%
A45
 
1.0%
N39
 
0.9%
T20
 
0.5%
I19
 
0.4%
E18
 
0.4%
M13
 
0.3%
L12
 
0.3%
Other values (9)30
 
0.7%

LONGITUD
Real number (ℝ)

HIGH CORRELATION

Distinct1052
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-101.8482702
Minimum-116.66425
Maximum-86.86412
Zeros0
Zeros (%)0.0%
Negative1054
Negative (%)100.0%
Memory size8.4 KiB
2022-11-15T17:21:23.889784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-116.66425
5-th percentile-112.9035282
Q1-105.38517
median-102.170665
Q3-98.971268
95-th percentile-89.32982835
Maximum-86.86412
Range29.80013
Interquartile range (IQR)6.413902

Descriptive statistics

Standard deviation6.697567532
Coefficient of variation (CV)-0.06576024823
Kurtosis-0.118011253
Mean-101.8482702
Median Absolute Deviation (MAD)3.20993
Skewness0.2088483039
Sum-107348.0768
Variance44.85741084
MonotonicityNot monotonic
2022-11-15T17:21:23.960025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-98.3503892
 
0.2%
-102.174882
 
0.2%
-102.02211
 
0.1%
-92.217121
 
0.1%
-96.001411
 
0.1%
-93.197941
 
0.1%
-93.167841
 
0.1%
-93.291371
 
0.1%
-92.340881
 
0.1%
-92.411331
 
0.1%
Other values (1042)1042
98.9%
ValueCountFrequency (%)
-116.664251
0.1%
-116.62181
0.1%
-116.6030281
0.1%
-116.5936391
0.1%
-116.580731
0.1%
-116.5785191
0.1%
-116.5728111
0.1%
-116.5011111
0.1%
-116.2473781
0.1%
-116.2255061
0.1%
ValueCountFrequency (%)
-86.864121
0.1%
-86.868881
0.1%
-86.877321
0.1%
-86.887141
0.1%
-86.901991
0.1%
-86.9808311
0.1%
-87.0303611
0.1%
-87.0349441
0.1%
-87.1472521
0.1%
-87.1569441
0.1%

LATITUD
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1053
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.16179596
Minimum14.56115
Maximum32.677713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2022-11-15T17:21:24.035936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum14.56115
5-th percentile18.0505385
Q120.2248575
median22.640705
Q325.50877
95-th percentile30.90901215
Maximum32.677713
Range18.116563
Interquartile range (IQR)5.2839125

Descriptive statistics

Standard deviation3.875005326
Coefficient of variation (CV)0.1673015915
Kurtosis-0.3676500265
Mean23.16179596
Median Absolute Deviation (MAD)2.64729
Skewness0.5265293123
Sum24412.53295
Variance15.01566628
MonotonicityNot monotonic
2022-11-15T17:21:24.117572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.2347782
 
0.2%
22.208871
 
0.1%
14.710281
 
0.1%
17.60121
 
0.1%
17.736591
 
0.1%
17.94741
 
0.1%
14.818631
 
0.1%
14.732641
 
0.1%
14.561151
 
0.1%
14.839551
 
0.1%
Other values (1043)1043
99.0%
ValueCountFrequency (%)
14.561151
0.1%
14.710281
0.1%
14.732641
0.1%
14.818631
0.1%
14.839551
0.1%
14.868921
0.1%
14.883541
0.1%
15.064111
0.1%
16.103931
0.1%
16.113691
0.1%
ValueCountFrequency (%)
32.6777131
0.1%
32.571651
0.1%
32.563581
0.1%
32.5013781
0.1%
32.4755551
0.1%
32.472231
0.1%
32.443771
0.1%
32.3947221
0.1%
32.3891671
0.1%
32.3319441
0.1%

PERIODO
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
2020.0
1054 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6324
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020.0
2nd row2020.0
3rd row2020.0
4th row2020.0
5th row2020.0

Common Values

ValueCountFrequency (%)
2020.01054
100.0%

Length

2022-11-15T17:21:24.196594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:24.258632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2020.01054
100.0%

Most occurring characters

ValueCountFrequency (%)
03162
50.0%
22108
33.3%
.1054
 
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5270
83.3%
Other Punctuation1054
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03162
60.0%
22108
40.0%
Other Punctuation
ValueCountFrequency (%)
.1054
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common6324
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03162
50.0%
22108
33.3%
.1054
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII6324
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03162
50.0%
22108
33.3%
.1054
 
16.7%

ALC_mg/L
Real number (ℝ≥0)

HIGH CORRELATION

Distinct808
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean234.6952657
Minimum26.64
Maximum1650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2022-11-15T17:21:24.319784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum26.64
5-th percentile93.4675
Q1164.2575
median215.825
Q3292.93
95-th percentile403.9475
Maximum1650
Range1623.36
Interquartile range (IQR)128.6725

Descriptive statistics

Standard deviation111.1478487
Coefficient of variation (CV)0.4735836848
Kurtosis27.2431692
Mean234.6952657
Median Absolute Deviation (MAD)61.605
Skewness2.935356858
Sum247368.81
Variance12353.84427
MonotonicityNot monotonic
2022-11-15T17:21:24.396557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157.625
 
0.5%
197.584
 
0.4%
168.724
 
0.4%
1614
 
0.4%
195.364
 
0.4%
257.854
 
0.4%
193.8154
 
0.4%
186.484
 
0.4%
204.7654
 
0.4%
183.153
 
0.3%
Other values (798)1014
96.2%
ValueCountFrequency (%)
26.641
0.1%
28.861
0.1%
47.041
0.1%
49.2751
0.1%
54.441
0.1%
56.611
0.1%
571
0.1%
58.261
0.1%
591
0.1%
60.7751
0.1%
ValueCountFrequency (%)
16501
0.1%
954.61
0.1%
764.661
0.1%
7521
0.1%
681.091
0.1%
6501
0.1%
646.021
0.1%
6261
0.1%
593.491
0.1%
5901
0.1%

CALIDAD_ALC
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
Alta
790 
Media
184 
Indeseable como FAAP
 
57
Baja
 
23

Length

Max length20
Median length4
Mean length5.039848197
Min length4

Characters and Unicode

Total characters5312
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlta
2nd rowAlta
3rd rowAlta
4th rowAlta
5th rowAlta

Common Values

ValueCountFrequency (%)
Alta790
75.0%
Media184
 
17.5%
Indeseable como FAAP57
 
5.4%
Baja23
 
2.2%

Length

2022-11-15T17:21:24.468371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:24.529081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
alta790
67.6%
media184
 
15.8%
indeseable57
 
4.9%
como57
 
4.9%
faap57
 
4.9%
baja23
 
2.0%

Most occurring characters

ValueCountFrequency (%)
a1077
20.3%
A904
17.0%
l847
15.9%
t790
14.9%
e355
 
6.7%
d241
 
4.5%
M184
 
3.5%
i184
 
3.5%
o114
 
2.1%
114
 
2.1%
Other values (10)502
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3916
73.7%
Uppercase Letter1282
 
24.1%
Space Separator114
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1077
27.5%
l847
21.6%
t790
20.2%
e355
 
9.1%
d241
 
6.2%
i184
 
4.7%
o114
 
2.9%
m57
 
1.5%
s57
 
1.5%
c57
 
1.5%
Other values (3)137
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
A904
70.5%
M184
 
14.4%
P57
 
4.4%
F57
 
4.4%
I57
 
4.4%
B23
 
1.8%
Space Separator
ValueCountFrequency (%)
114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5198
97.9%
Common114
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1077
20.7%
A904
17.4%
l847
16.3%
t790
15.2%
e355
 
6.8%
d241
 
4.6%
M184
 
3.5%
i184
 
3.5%
o114
 
2.2%
P57
 
1.1%
Other values (9)445
8.6%
Common
ValueCountFrequency (%)
114
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1077
20.3%
A904
17.0%
l847
15.9%
t790
14.9%
e355
 
6.7%
d241
 
4.5%
M184
 
3.5%
i184
 
3.5%
o114
 
2.1%
114
 
2.1%
Other values (10)502
9.5%

CONDUCT_mS/cm
Real number (ℝ≥0)

HIGH CORRELATION

Distinct797
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1142.726471
Minimum110
Maximum18577
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2022-11-15T17:21:24.590480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile268
Q1506
median820
Q31328
95-th percentile2907
Maximum18577
Range18467
Interquartile range (IQR)822

Descriptive statistics

Standard deviation1248.990617
Coefficient of variation (CV)1.09299176
Kurtosis59.63933736
Mean1142.726471
Median Absolute Deviation (MAD)377
Skewness5.989700907
Sum1204433.7
Variance1559977.56
MonotonicityNot monotonic
2022-11-15T17:21:24.662492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7776
 
0.6%
5774
 
0.4%
3084
 
0.4%
4544
 
0.4%
3004
 
0.4%
5984
 
0.4%
4124
 
0.4%
2923
 
0.3%
3263
 
0.3%
4363
 
0.3%
Other values (787)1015
96.3%
ValueCountFrequency (%)
1101
0.1%
112.81
0.1%
117.71
0.1%
1241
0.1%
1261
0.1%
139.31
0.1%
1401
0.1%
1641
0.1%
1701
0.1%
1731
0.1%
ValueCountFrequency (%)
185771
0.1%
143501
0.1%
128301
0.1%
97401
0.1%
86801
0.1%
83001
0.1%
69001
0.1%
66701
0.1%
60411
0.1%
57911
0.1%

CALIDAD_CONDUC
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
Permisible para riego
458 
Buena para riego
429 
Dudosa para riego
72 
Indeseable para riego
51 
Excelente para riego
 
44

Length

Max length21
Median length20
Mean length18.64990512
Min length16

Characters and Unicode

Total characters19657
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPermisible para riego
2nd rowBuena para riego
3rd rowBuena para riego
4th rowBuena para riego
5th rowPermisible para riego

Common Values

ValueCountFrequency (%)
Permisible para riego458
43.5%
Buena para riego429
40.7%
Dudosa para riego72
 
6.8%
Indeseable para riego51
 
4.8%
Excelente para riego44
 
4.2%

Length

2022-11-15T17:21:24.730307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:24.790148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
para1054
33.3%
riego1054
33.3%
permisible458
14.5%
buena429
13.6%
dudosa72
 
2.3%
indeseable51
 
1.6%
excelente44
 
1.4%

Most occurring characters

ValueCountFrequency (%)
e2684
13.7%
a2660
13.5%
r2566
13.1%
2108
10.7%
i1970
10.0%
o1126
 
5.7%
g1054
 
5.4%
p1054
 
5.4%
s581
 
3.0%
l553
 
2.8%
Other values (13)3301
16.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter16495
83.9%
Space Separator2108
 
10.7%
Uppercase Letter1054
 
5.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2684
16.3%
a2660
16.1%
r2566
15.6%
i1970
11.9%
o1126
6.8%
g1054
 
6.4%
p1054
 
6.4%
s581
 
3.5%
l553
 
3.4%
n524
 
3.2%
Other values (7)1723
10.4%
Uppercase Letter
ValueCountFrequency (%)
P458
43.5%
B429
40.7%
D72
 
6.8%
I51
 
4.8%
E44
 
4.2%
Space Separator
ValueCountFrequency (%)
2108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17549
89.3%
Common2108
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2684
15.3%
a2660
15.2%
r2566
14.6%
i1970
11.2%
o1126
6.4%
g1054
 
6.0%
p1054
 
6.0%
s581
 
3.3%
l553
 
3.2%
n524
 
3.0%
Other values (12)2777
15.8%
Common
ValueCountFrequency (%)
2108
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII19657
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2684
13.7%
a2660
13.5%
r2566
13.1%
2108
10.7%
i1970
10.0%
o1126
 
5.7%
g1054
 
5.4%
p1054
 
5.4%
s581
 
3.0%
l553
 
2.8%
Other values (13)3301
16.8%

SDT_M_mg/L
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct914
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean896.9457971
Minimum101.2
Maximum82170
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2022-11-15T17:21:24.858232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum101.2
5-th percentile204
Q1338.05
median551.4
Q3915.6
95-th percentile2281.85
Maximum82170
Range82068.8
Interquartile range (IQR)577.55

Descriptive statistics

Standard deviation2765.757924
Coefficient of variation (CV)3.083528495
Kurtosis717.3082185
Mean896.9457971
Median Absolute Deviation (MAD)248.6
Skewness25.08739509
Sum945380.8701
Variance7649416.892
MonotonicityNot monotonic
2022-11-15T17:21:24.927955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3204
 
0.4%
3174
 
0.4%
3804
 
0.4%
4964
 
0.4%
2924
 
0.4%
6043
 
0.3%
4863
 
0.3%
6323
 
0.3%
3393
 
0.3%
2683
 
0.3%
Other values (904)1019
96.7%
ValueCountFrequency (%)
101.21
0.1%
1041
0.1%
1061
0.1%
111.41
0.1%
1121
0.1%
129.61
0.1%
1361
0.1%
138.21
0.1%
139.41
0.1%
1421
0.1%
ValueCountFrequency (%)
821701
0.1%
27215.81
0.1%
101961
0.1%
9503.66671
0.1%
8230.41
0.1%
77461
0.1%
5418.21
0.1%
46761
0.1%
43501
0.1%
41961
0.1%

CALIDAD_SDT_ra
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
Excelente para riego
485 
Cultivos sensibles
341 
Cultivos con manejo especial
158 
Cultivos tolerantes
63 
Indeseable para riego
 
7

Length

Max length28
Median length21
Mean length20.49905123
Min length18

Characters and Unicode

Total characters21606
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCultivos sensibles
2nd rowExcelente para riego
3rd rowExcelente para riego
4th rowExcelente para riego
5th rowCultivos con manejo especial

Common Values

ValueCountFrequency (%)
Excelente para riego485
46.0%
Cultivos sensibles341
32.4%
Cultivos con manejo especial158
 
15.0%
Cultivos tolerantes63
 
6.0%
Indeseable para riego7
 
0.7%

Length

2022-11-15T17:21:24.991735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:25.156347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
cultivos562
19.3%
para492
16.9%
riego492
16.9%
excelente485
16.6%
sensibles341
11.7%
con158
 
5.4%
manejo158
 
5.4%
especial158
 
5.4%
tolerantes63
 
2.2%
indeseable7
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e3250
15.0%
1862
 
8.6%
s1813
 
8.4%
l1616
 
7.5%
i1553
 
7.2%
o1433
 
6.6%
a1370
 
6.3%
n1212
 
5.6%
t1173
 
5.4%
r1047
 
4.8%
Other values (13)5277
24.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter18690
86.5%
Space Separator1862
 
8.6%
Uppercase Letter1054
 
4.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3250
17.4%
s1813
9.7%
l1616
8.6%
i1553
8.3%
o1433
7.7%
a1370
7.3%
n1212
 
6.5%
t1173
 
6.3%
r1047
 
5.6%
c801
 
4.3%
Other values (9)3422
18.3%
Uppercase Letter
ValueCountFrequency (%)
C562
53.3%
E485
46.0%
I7
 
0.7%
Space Separator
ValueCountFrequency (%)
1862
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin19744
91.4%
Common1862
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3250
16.5%
s1813
9.2%
l1616
 
8.2%
i1553
 
7.9%
o1433
 
7.3%
a1370
 
6.9%
n1212
 
6.1%
t1173
 
5.9%
r1047
 
5.3%
c801
 
4.1%
Other values (12)4476
22.7%
Common
ValueCountFrequency (%)
1862
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII21606
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e3250
15.0%
1862
 
8.6%
s1813
 
8.4%
l1616
 
7.5%
i1553
 
7.2%
o1433
 
6.6%
a1370
 
6.3%
n1212
 
5.6%
t1173
 
5.4%
r1047
 
4.8%
Other values (13)5277
24.4%

CALIDAD_SDT_salin
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
Potable - Dulce
826 
Ligeramente salobres
158 
Salobres
 
67
Salinas
 
3

Length

Max length20
Median length15
Mean length15.28178368
Min length7

Characters and Unicode

Total characters16107
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPotable - Dulce
2nd rowPotable - Dulce
3rd rowPotable - Dulce
4th rowPotable - Dulce
5th rowLigeramente salobres

Common Values

ValueCountFrequency (%)
Potable - Dulce826
78.4%
Ligeramente salobres158
 
15.0%
Salobres67
 
6.4%
Salinas3
 
0.3%

Length

2022-11-15T17:21:25.218868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:25.279262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
potable826
28.8%
826
28.8%
dulce826
28.8%
salobres225
 
7.9%
ligeramente158
 
5.5%
salinas3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e2351
14.6%
l1880
11.7%
1810
11.2%
a1215
 
7.5%
o1051
 
6.5%
b1051
 
6.5%
t984
 
6.1%
P826
 
5.1%
c826
 
5.1%
u826
 
5.1%
Other values (10)3287
20.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter11591
72.0%
Uppercase Letter1880
 
11.7%
Space Separator1810
 
11.2%
Dash Punctuation826
 
5.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2351
20.3%
l1880
16.2%
a1215
10.5%
o1051
9.1%
b1051
9.1%
t984
8.5%
c826
 
7.1%
u826
 
7.1%
s386
 
3.3%
r383
 
3.3%
Other values (4)638
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
P826
43.9%
D826
43.9%
L158
 
8.4%
S70
 
3.7%
Space Separator
ValueCountFrequency (%)
1810
100.0%
Dash Punctuation
ValueCountFrequency (%)
-826
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13471
83.6%
Common2636
 
16.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2351
17.5%
l1880
14.0%
a1215
9.0%
o1051
7.8%
b1051
7.8%
t984
7.3%
P826
 
6.1%
c826
 
6.1%
u826
 
6.1%
D826
 
6.1%
Other values (8)1635
12.1%
Common
ValueCountFrequency (%)
1810
68.7%
-826
31.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII16107
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2351
14.6%
l1880
11.7%
1810
11.2%
a1215
 
7.5%
o1051
 
6.5%
b1051
 
6.5%
t984
 
6.1%
P826
 
5.1%
c826
 
5.1%
u826
 
5.1%
Other values (10)3287
20.4%

FLUORUROS_mg/L
Real number (ℝ≥0)

HIGH CORRELATION

Distinct853
Distinct (%)80.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.078547059
Minimum0.2
Maximum34.8033
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2022-11-15T17:21:25.342601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.2
Q10.269475
median0.50695
Q31.1424
95-th percentile3.72488
Maximum34.8033
Range34.6033
Interquartile range (IQR)0.872925

Descriptive statistics

Standard deviation1.931203929
Coefficient of variation (CV)1.790560656
Kurtosis108.5039377
Mean1.078547059
Median Absolute Deviation (MAD)0.30215
Skewness8.243262324
Sum1136.7886
Variance3.729548616
MonotonicityNot monotonic
2022-11-15T17:21:25.416652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2158
 
15.0%
0.4663
 
0.3%
0.52023
 
0.3%
0.21582
 
0.2%
0.2192
 
0.2%
0.2442
 
0.2%
0.2412
 
0.2%
0.5332
 
0.2%
0.53872
 
0.2%
0.8052
 
0.2%
Other values (843)876
83.1%
ValueCountFrequency (%)
0.2158
15.0%
0.20081
 
0.1%
0.2021
 
0.1%
0.20351
 
0.1%
0.2041
 
0.1%
0.20421
 
0.1%
0.20542
 
0.2%
0.20651
 
0.1%
0.2071
 
0.1%
0.20721
 
0.1%
ValueCountFrequency (%)
34.80331
0.1%
21.23751
0.1%
15.42431
0.1%
13.00781
0.1%
12.5011
0.1%
12.4781
0.1%
12.29331
0.1%
12.17121
0.1%
11.63231
0.1%
10.58321
0.1%

CALIDAD_FLUO
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
Baja
426 
Potable - Optima
225 
Media
213 
Alta
190 

Length

Max length16
Median length4
Mean length6.763757116
Min length4

Characters and Unicode

Total characters7129
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPotable - Optima
2nd rowPotable - Optima
3rd rowAlta
4th rowPotable - Optima
5th rowBaja

Common Values

ValueCountFrequency (%)
Baja426
40.4%
Potable - Optima225
21.3%
Media213
20.2%
Alta190
18.0%

Length

2022-11-15T17:21:25.490245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:25.553989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
baja426
28.3%
potable225
15.0%
225
15.0%
optima225
15.0%
media213
14.2%
alta190
12.6%

Most occurring characters

ValueCountFrequency (%)
a1705
23.9%
t640
 
9.0%
450
 
6.3%
i438
 
6.1%
e438
 
6.1%
B426
 
6.0%
j426
 
6.0%
l415
 
5.8%
b225
 
3.2%
o225
 
3.2%
Other values (8)1741
24.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5175
72.6%
Uppercase Letter1279
 
17.9%
Space Separator450
 
6.3%
Dash Punctuation225
 
3.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1705
32.9%
t640
 
12.4%
i438
 
8.5%
e438
 
8.5%
j426
 
8.2%
l415
 
8.0%
b225
 
4.3%
o225
 
4.3%
p225
 
4.3%
m225
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
B426
33.3%
P225
17.6%
O225
17.6%
M213
16.7%
A190
14.9%
Space Separator
ValueCountFrequency (%)
450
100.0%
Dash Punctuation
ValueCountFrequency (%)
-225
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6454
90.5%
Common675
 
9.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1705
26.4%
t640
 
9.9%
i438
 
6.8%
e438
 
6.8%
B426
 
6.6%
j426
 
6.6%
l415
 
6.4%
b225
 
3.5%
o225
 
3.5%
P225
 
3.5%
Other values (6)1291
20.0%
Common
ValueCountFrequency (%)
450
66.7%
-225
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII7129
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1705
23.9%
t640
 
9.0%
450
 
6.3%
i438
 
6.1%
e438
 
6.1%
B426
 
6.0%
j426
 
6.0%
l415
 
5.8%
b225
 
3.2%
o225
 
3.2%
Other values (8)1741
24.4%

DUR_mg/L
Real number (ℝ≥0)

HIGH CORRELATION

Distinct881
Distinct (%)83.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean349.8935844
Minimum20
Maximum3810.6922
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2022-11-15T17:21:25.619810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile41.63145
Q1121.512
median245.99445
Q3455.6172
95-th percentile1000.534
Maximum3810.6922
Range3790.6922
Interquartile range (IQR)334.1052

Descriptive statistics

Standard deviation360.9601532
Coefficient of variation (CV)1.031628384
Kurtosis20.41701095
Mean349.8935844
Median Absolute Deviation (MAD)143.95985
Skewness3.397190179
Sum368787.838
Variance130292.2322
MonotonicityNot monotonic
2022-11-15T17:21:25.695102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2025
 
2.4%
121.5125
 
0.5%
53.85424
 
0.4%
109.564
 
0.4%
148.443
 
0.3%
81.6723
 
0.3%
616.7143
 
0.3%
3163
 
0.3%
3463
 
0.3%
39.843
 
0.3%
Other values (871)998
94.7%
ValueCountFrequency (%)
2025
2.4%
21.94061
 
0.1%
23.681
 
0.1%
23.9041
 
0.1%
24.87251
 
0.1%
25.72961
 
0.1%
25.8961
 
0.1%
261
 
0.1%
27.70881
 
0.1%
27.8881
 
0.1%
ValueCountFrequency (%)
3810.69221
0.1%
3426.241
0.1%
3302.4041
0.1%
27241
0.1%
21531
0.1%
2001.961
0.1%
1968.03131
0.1%
1889.9451
0.1%
1790.461
0.1%
1661.1491
0.1%

CALIDAD_DUR
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
Potable - Dura
572 
Muy dura e indeseable usos industrial y domestico
225 
Potable - Moderadamente suave
165 
Potable - Suave
92 

Length

Max length49
Median length14
Mean length23.90702087
Min length14

Characters and Unicode

Total characters25198
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPotable - Dura
2nd rowPotable - Dura
3rd rowPotable - Dura
4th rowPotable - Dura
5th rowPotable - Dura

Common Values

ValueCountFrequency (%)
Potable - Dura572
54.3%
Muy dura e indeseable usos industrial y domestico225
 
21.3%
Potable - Moderadamente suave165
 
15.7%
Potable - Suave92
 
8.7%

Length

2022-11-15T17:21:25.768205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:25.833107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
potable829
18.6%
829
18.6%
dura797
17.9%
suave257
 
5.8%
muy225
 
5.1%
e225
 
5.1%
indeseable225
 
5.1%
usos225
 
5.1%
industrial225
 
5.1%
y225
 
5.1%
Other values (2)390
8.8%

Most occurring characters

ValueCountFrequency (%)
3398
13.5%
e2706
10.7%
a2663
10.6%
u1729
 
6.9%
o1669
 
6.6%
t1444
 
5.7%
s1290
 
5.1%
l1279
 
5.1%
d1230
 
4.9%
r1187
 
4.7%
Other values (12)6603
26.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter19088
75.8%
Space Separator3398
 
13.5%
Uppercase Letter1883
 
7.5%
Dash Punctuation829
 
3.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2706
14.2%
a2663
14.0%
u1729
9.1%
o1669
8.7%
t1444
7.6%
s1290
6.8%
l1279
6.7%
d1230
6.4%
r1187
6.2%
b1054
 
5.5%
Other values (6)2837
14.9%
Uppercase Letter
ValueCountFrequency (%)
P829
44.0%
D572
30.4%
M390
20.7%
S92
 
4.9%
Space Separator
ValueCountFrequency (%)
3398
100.0%
Dash Punctuation
ValueCountFrequency (%)
-829
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin20971
83.2%
Common4227
 
16.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2706
12.9%
a2663
12.7%
u1729
 
8.2%
o1669
 
8.0%
t1444
 
6.9%
s1290
 
6.2%
l1279
 
6.1%
d1230
 
5.9%
r1187
 
5.7%
b1054
 
5.0%
Other values (10)4720
22.5%
Common
ValueCountFrequency (%)
3398
80.4%
-829
 
19.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII25198
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3398
13.5%
e2706
10.7%
a2663
10.6%
u1729
 
6.9%
o1669
 
6.6%
t1444
 
5.7%
s1290
 
5.1%
l1279
 
5.1%
d1230
 
4.9%
r1187
 
4.7%
Other values (12)6603
26.2%

COLI_FEC_NMP/100_mL
Real number (ℝ≥0)

HIGH CORRELATION

Distinct124
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean359.7341556
Minimum1.1
Maximum24196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2022-11-15T17:21:25.905040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile1.1
Q11.1
median1.1
Q310.75
95-th percentile1138.6
Maximum24196
Range24194.9
Interquartile range (IQR)9.65

Descriptive statistics

Standard deviation2065.705773
Coefficient of variation (CV)5.742312041
Kurtosis87.55671698
Mean359.7341556
Median Absolute Deviation (MAD)0
Skewness8.897613829
Sum379159.8
Variance4267140.341
MonotonicityNot monotonic
2022-11-15T17:21:25.984290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1728
69.1%
1037
 
3.5%
4028
 
2.7%
2019
 
1.8%
3111
 
1.0%
4111
 
1.0%
410
 
0.9%
99
 
0.9%
756
 
0.6%
236
 
0.6%
Other values (114)189
 
17.9%
ValueCountFrequency (%)
1.1728
69.1%
32
 
0.2%
410
 
0.9%
74
 
0.4%
99
 
0.9%
1037
 
3.5%
112
 
0.2%
2019
 
1.8%
211
 
0.1%
236
 
0.6%
ValueCountFrequency (%)
241964
0.4%
198631
 
0.1%
173292
0.2%
141362
0.2%
111991
 
0.1%
110001
 
0.1%
104621
 
0.1%
92081
 
0.1%
81611
 
0.1%
72701
 
0.1%

CALIDAD_COLI_FEC
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
Potable - Excelente
730 
Buena calidad
204 
Aceptable
 
59
Contaminada
 
49
Fuertemente contaminada
 
12

Length

Max length23
Median length19
Mean length16.95256167
Min length9

Characters and Unicode

Total characters17868
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPotable - Excelente
2nd rowPotable - Excelente
3rd rowPotable - Excelente
4th rowPotable - Excelente
5th rowAceptable

Common Values

ValueCountFrequency (%)
Potable - Excelente730
69.3%
Buena calidad204
 
19.4%
Aceptable59
 
5.6%
Contaminada49
 
4.6%
Fuertemente contaminada12
 
1.1%

Length

2022-11-15T17:21:26.054343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:26.118480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
potable730
26.7%
730
26.7%
excelente730
26.7%
buena204
 
7.5%
calidad204
 
7.5%
contaminada61
 
2.2%
aceptable59
 
2.2%
fuertemente12
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e3290
18.4%
l1723
9.6%
1676
9.4%
t1604
9.0%
a1584
8.9%
n1068
 
6.0%
c1005
 
5.6%
o791
 
4.4%
b789
 
4.4%
x730
 
4.1%
Other values (13)3608
20.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13678
76.6%
Uppercase Letter1784
 
10.0%
Space Separator1676
 
9.4%
Dash Punctuation730
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3290
24.1%
l1723
12.6%
t1604
11.7%
a1584
11.6%
n1068
 
7.8%
c1005
 
7.3%
o791
 
5.8%
b789
 
5.8%
x730
 
5.3%
d469
 
3.4%
Other values (5)625
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
P730
40.9%
E730
40.9%
B204
 
11.4%
A59
 
3.3%
C49
 
2.7%
F12
 
0.7%
Space Separator
ValueCountFrequency (%)
1676
100.0%
Dash Punctuation
ValueCountFrequency (%)
-730
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15462
86.5%
Common2406
 
13.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3290
21.3%
l1723
11.1%
t1604
10.4%
a1584
10.2%
n1068
 
6.9%
c1005
 
6.5%
o791
 
5.1%
b789
 
5.1%
x730
 
4.7%
P730
 
4.7%
Other values (11)2148
13.9%
Common
ValueCountFrequency (%)
1676
69.7%
-730
30.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII17868
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e3290
18.4%
l1723
9.6%
1676
9.4%
t1604
9.0%
a1584
8.9%
n1068
 
6.0%
c1005
 
5.6%
o791
 
4.4%
b789
 
4.4%
x730
 
4.1%
Other values (13)3608
20.2%

N_NO3_mg/L
Real number (ℝ≥0)

HIGH CORRELATION

Distinct983
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.321651351
Minimum0.02
Maximum121.007813
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2022-11-15T17:21:26.186134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.02
Q10.65166675
median2.082916
Q35.190385
95-th percentile13.9296595
Maximum121.007813
Range120.987813
Interquartile range (IQR)4.53871825

Descriptive statistics

Standard deviation8.378331909
Coefficient of variation (CV)1.938687605
Kurtosis70.43611329
Mean4.321651351
Median Absolute Deviation (MAD)1.8523215
Skewness7.041112737
Sum4555.020524
Variance70.19644557
MonotonicityNot monotonic
2022-11-15T17:21:26.266014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0264
 
6.1%
0.0963
 
0.3%
1.9552
 
0.2%
0.21622
 
0.2%
0.0292
 
0.2%
0.1472
 
0.2%
0.1632
 
0.2%
0.0732
 
0.2%
5.85061
 
0.1%
9.95381
 
0.1%
Other values (973)973
92.3%
ValueCountFrequency (%)
0.0264
6.1%
0.02081
 
0.1%
0.0211
 
0.1%
0.0220591
 
0.1%
0.0241
 
0.1%
0.0271
 
0.1%
0.0273561
 
0.1%
0.0292
 
0.2%
0.029741
 
0.1%
0.03071
 
0.1%
ValueCountFrequency (%)
121.0078131
0.1%
102.2994971
0.1%
77.3921
0.1%
69.8813221
0.1%
67.2499181
0.1%
62.9587461
0.1%
58.7383551
0.1%
52.5062831
0.1%
49.9083841
0.1%
36.4771041
0.1%

CALIDAD_N_NO3
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
Potable - Excelente
780 
Potable - Buena calidad
194 
No apta como FAAP
80 

Length

Max length23
Median length19
Mean length19.58444023
Min length17

Characters and Unicode

Total characters20642
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPotable - Excelente
2nd rowPotable - Buena calidad
3rd rowPotable - Excelente
4th rowPotable - Excelente
5th rowNo apta como FAAP

Common Values

ValueCountFrequency (%)
Potable - Excelente780
74.0%
Potable - Buena calidad194
 
18.4%
No apta como FAAP80
 
7.6%

Length

2022-11-15T17:21:26.338041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:26.400715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
potable974
28.3%
974
28.3%
excelente780
22.7%
buena194
 
5.6%
calidad194
 
5.6%
no80
 
2.3%
apta80
 
2.3%
como80
 
2.3%
faap80
 
2.3%

Most occurring characters

ValueCountFrequency (%)
e3508
17.0%
2382
11.5%
l1948
9.4%
t1834
8.9%
a1716
8.3%
o1214
 
5.9%
P1054
 
5.1%
c1054
 
5.1%
-974
 
4.7%
b974
 
4.7%
Other values (12)3984
19.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14938
72.4%
Space Separator2382
 
11.5%
Uppercase Letter2348
 
11.4%
Dash Punctuation974
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3508
23.5%
l1948
13.0%
t1834
12.3%
a1716
11.5%
o1214
 
8.1%
c1054
 
7.1%
b974
 
6.5%
n974
 
6.5%
x780
 
5.2%
d388
 
2.6%
Other values (4)548
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
P1054
44.9%
E780
33.2%
B194
 
8.3%
A160
 
6.8%
N80
 
3.4%
F80
 
3.4%
Space Separator
ValueCountFrequency (%)
2382
100.0%
Dash Punctuation
ValueCountFrequency (%)
-974
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17286
83.7%
Common3356
 
16.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3508
20.3%
l1948
11.3%
t1834
10.6%
a1716
9.9%
o1214
 
7.0%
P1054
 
6.1%
c1054
 
6.1%
b974
 
5.6%
n974
 
5.6%
E780
 
4.5%
Other values (10)2230
12.9%
Common
ValueCountFrequency (%)
2382
71.0%
-974
29.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII20642
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e3508
17.0%
2382
11.5%
l1948
9.4%
t1834
8.9%
a1716
8.3%
o1214
 
5.9%
P1054
 
5.1%
c1054
 
5.1%
-974
 
4.7%
b974
 
4.7%
Other values (12)3984
19.3%

AS_TOT_mg/L
Real number (ℝ≥0)

HIGH CORRELATION

Distinct206
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0195036148
Minimum0.01
Maximum0.4522
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2022-11-15T17:21:26.466838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.01
median0.01
Q30.01
95-th percentile0.066385
Maximum0.4522
Range0.4422
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.03505077194
Coefficient of variation (CV)1.79714234
Kurtosis58.7151296
Mean0.0195036148
Median Absolute Deviation (MAD)0
Skewness6.761573336
Sum20.55681
Variance0.001228556614
MonotonicityNot monotonic
2022-11-15T17:21:26.540292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01805
76.4%
0.01354
 
0.4%
0.02013
 
0.3%
0.02173
 
0.3%
0.01183
 
0.3%
0.01543
 
0.3%
0.02263
 
0.3%
0.04382
 
0.2%
0.02382
 
0.2%
0.01052
 
0.2%
Other values (196)224
 
21.3%
ValueCountFrequency (%)
0.01805
76.4%
0.01011
 
0.1%
0.010151
 
0.1%
0.01031
 
0.1%
0.01041
 
0.1%
0.01052
 
0.2%
0.01062
 
0.2%
0.01071
 
0.1%
0.01082
 
0.2%
0.01092
 
0.2%
ValueCountFrequency (%)
0.45221
0.1%
0.37841
0.1%
0.36881
0.1%
0.35581
0.1%
0.28871
0.1%
0.25491
0.1%
0.25181
0.1%
0.20011
0.1%
0.19571
0.1%
0.19161
0.1%

CALIDAD_AS
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
Potable - Excelente
805 
No apta como FAAP
125 
Apta como FAAP
124 

Length

Max length19
Median length19
Mean length18.17457306
Min length14

Characters and Unicode

Total characters19156
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApta como FAAP
2nd rowApta como FAAP
3rd rowNo apta como FAAP
4th rowApta como FAAP
5th rowPotable - Excelente

Common Values

ValueCountFrequency (%)
Potable - Excelente805
76.4%
No apta como FAAP125
 
11.9%
Apta como FAAP124
 
11.8%

Length

2022-11-15T17:21:26.606144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:26.667359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
potable805
24.5%
805
24.5%
excelente805
24.5%
apta249
 
7.6%
como249
 
7.6%
faap249
 
7.6%
no125
 
3.8%

Most occurring characters

ValueCountFrequency (%)
e3220
16.8%
2233
11.7%
t1859
9.7%
l1610
8.4%
o1428
 
7.5%
a1179
 
6.2%
P1054
 
5.5%
c1054
 
5.5%
x805
 
4.2%
n805
 
4.2%
Other values (8)3909
20.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13263
69.2%
Uppercase Letter2855
 
14.9%
Space Separator2233
 
11.7%
Dash Punctuation805
 
4.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3220
24.3%
t1859
14.0%
l1610
12.1%
o1428
10.8%
a1179
 
8.9%
c1054
 
7.9%
x805
 
6.1%
n805
 
6.1%
b805
 
6.1%
p249
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
P1054
36.9%
E805
28.2%
A622
21.8%
F249
 
8.7%
N125
 
4.4%
Space Separator
ValueCountFrequency (%)
2233
100.0%
Dash Punctuation
ValueCountFrequency (%)
-805
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16118
84.1%
Common3038
 
15.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3220
20.0%
t1859
11.5%
l1610
10.0%
o1428
8.9%
a1179
 
7.3%
P1054
 
6.5%
c1054
 
6.5%
x805
 
5.0%
n805
 
5.0%
E805
 
5.0%
Other values (6)2299
14.3%
Common
ValueCountFrequency (%)
2233
73.5%
-805
 
26.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII19156
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e3220
16.8%
2233
11.7%
t1859
9.7%
l1610
8.4%
o1428
 
7.5%
a1179
 
6.2%
P1054
 
5.5%
c1054
 
5.5%
x805
 
4.2%
n805
 
4.2%
Other values (8)3909
20.4%

CD_TOT_mg/L
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
0.003
1052 
0.0056
 
1
0.03211
 
1

Length

Max length7
Median length5
Mean length5.0028463
Min length5

Characters and Unicode

Total characters5273
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row0.003
2nd row0.003
3rd row0.003
4th row0.003
5th row0.003

Common Values

ValueCountFrequency (%)
0.0031052
99.8%
0.00561
 
0.1%
0.032111
 
0.1%

Length

2022-11-15T17:21:26.724095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:26.783876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0031052
99.8%
0.00561
 
0.1%
0.032111
 
0.1%

Most occurring characters

ValueCountFrequency (%)
03161
59.9%
.1054
 
20.0%
31053
 
20.0%
12
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
21
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4219
80.0%
Other Punctuation1054
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03161
74.9%
31053
 
25.0%
12
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
21
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.1054
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5273
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03161
59.9%
.1054
 
20.0%
31053
 
20.0%
12
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
21
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5273
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03161
59.9%
.1054
 
20.0%
31053
 
20.0%
12
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
21
 
< 0.1%

CALIDAD_CD
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
Potable - Excelente
1052 
No apta como FAAP
 
2

Length

Max length19
Median length19
Mean length18.99620493
Min length17

Characters and Unicode

Total characters20022
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPotable - Excelente
2nd rowPotable - Excelente
3rd rowPotable - Excelente
4th rowPotable - Excelente
5th rowPotable - Excelente

Common Values

ValueCountFrequency (%)
Potable - Excelente1052
99.8%
No apta como FAAP2
 
0.2%

Length

2022-11-15T17:21:26.835148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:26.894654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
potable1052
33.2%
1052
33.2%
excelente1052
33.2%
no2
 
0.1%
apta2
 
0.1%
como2
 
0.1%
faap2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e4208
21.0%
2110
10.5%
t2106
10.5%
l2104
10.5%
o1058
 
5.3%
a1056
 
5.3%
P1054
 
5.3%
c1054
 
5.3%
x1052
 
5.3%
n1052
 
5.3%
Other values (8)3168
15.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14746
73.6%
Uppercase Letter2114
 
10.6%
Space Separator2110
 
10.5%
Dash Punctuation1052
 
5.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4208
28.5%
t2106
14.3%
l2104
14.3%
o1058
 
7.2%
a1056
 
7.2%
c1054
 
7.1%
x1052
 
7.1%
n1052
 
7.1%
b1052
 
7.1%
p2
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
P1054
49.9%
E1052
49.8%
A4
 
0.2%
N2
 
0.1%
F2
 
0.1%
Space Separator
ValueCountFrequency (%)
2110
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1052
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16860
84.2%
Common3162
 
15.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4208
25.0%
t2106
12.5%
l2104
12.5%
o1058
 
6.3%
a1056
 
6.3%
P1054
 
6.3%
c1054
 
6.3%
x1052
 
6.2%
n1052
 
6.2%
E1052
 
6.2%
Other values (6)1064
 
6.3%
Common
ValueCountFrequency (%)
2110
66.7%
-1052
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII20022
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4208
21.0%
2110
10.5%
t2106
10.5%
l2104
10.5%
o1058
 
5.3%
a1056
 
5.3%
P1054
 
5.3%
c1054
 
5.3%
x1052
 
5.3%
n1052
 
5.3%
Other values (8)3168
15.8%

CR_TOT_mg/L
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct164
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01335270398
Minimum0.005
Maximum5.0032
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2022-11-15T17:21:27.057000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.005
5-th percentile0.005
Q10.005
median0.005
Q30.005
95-th percentile0.021238
Maximum5.0032
Range4.9982
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1554116727
Coefficient of variation (CV)11.63896637
Kurtosis1012.163783
Mean0.01335270398
Median Absolute Deviation (MAD)0
Skewness31.54467997
Sum14.07375
Variance0.024152788
MonotonicityNot monotonic
2022-11-15T17:21:27.130830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.005851
80.7%
0.00516
 
0.6%
0.00535
 
0.5%
0.00524
 
0.4%
0.0074
 
0.4%
0.00993
 
0.3%
0.013
 
0.3%
0.00713
 
0.3%
0.0143
 
0.3%
0.00673
 
0.3%
Other values (154)169
 
16.0%
ValueCountFrequency (%)
0.005851
80.7%
0.00516
 
0.6%
0.00524
 
0.4%
0.00535
 
0.5%
0.00542
 
0.2%
0.00552
 
0.2%
0.00562
 
0.2%
0.00571
 
0.1%
0.00581
 
0.1%
0.0063
 
0.3%
ValueCountFrequency (%)
5.00321
0.1%
0.5911
0.1%
0.150941
0.1%
0.146111
0.1%
0.138741
0.1%
0.129011
0.1%
0.10991
0.1%
0.109751
0.1%
0.10741
0.1%
0.094991
0.1%

CALIDAD_CR
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
Potable - Excelente
1039 
No apta como FAAP
 
15

Length

Max length19
Median length19
Mean length18.971537
Min length17

Characters and Unicode

Total characters19996
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPotable - Excelente
2nd rowPotable - Excelente
3rd rowPotable - Excelente
4th rowPotable - Excelente
5th rowPotable - Excelente

Common Values

ValueCountFrequency (%)
Potable - Excelente1039
98.6%
No apta como FAAP15
 
1.4%

Length

2022-11-15T17:21:27.197448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:27.256409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
potable1039
32.7%
1039
32.7%
excelente1039
32.7%
no15
 
0.5%
apta15
 
0.5%
como15
 
0.5%
faap15
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e4156
20.8%
2123
10.6%
t2093
10.5%
l2078
10.4%
o1084
 
5.4%
a1069
 
5.3%
P1054
 
5.3%
c1054
 
5.3%
x1039
 
5.2%
n1039
 
5.2%
Other values (8)3207
16.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14681
73.4%
Uppercase Letter2153
 
10.8%
Space Separator2123
 
10.6%
Dash Punctuation1039
 
5.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4156
28.3%
t2093
14.3%
l2078
14.2%
o1084
 
7.4%
a1069
 
7.3%
c1054
 
7.2%
x1039
 
7.1%
n1039
 
7.1%
b1039
 
7.1%
p15
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
P1054
49.0%
E1039
48.3%
A30
 
1.4%
N15
 
0.7%
F15
 
0.7%
Space Separator
ValueCountFrequency (%)
2123
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1039
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16834
84.2%
Common3162
 
15.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4156
24.7%
t2093
12.4%
l2078
12.3%
o1084
 
6.4%
a1069
 
6.4%
P1054
 
6.3%
c1054
 
6.3%
x1039
 
6.2%
n1039
 
6.2%
E1039
 
6.2%
Other values (6)1129
 
6.7%
Common
ValueCountFrequency (%)
2123
67.1%
-1039
32.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII19996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4156
20.8%
2123
10.6%
t2093
10.5%
l2078
10.4%
o1084
 
5.4%
a1069
 
5.3%
P1054
 
5.3%
c1054
 
5.3%
x1039
 
5.2%
n1039
 
5.2%
Other values (8)3207
16.0%

HG_TOT_mg/L
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct59
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0005572201139
Minimum0.0005
Maximum0.01415
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2022-11-15T17:21:27.312368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0005
5-th percentile0.0005
Q10.0005
median0.0005
Q30.0005
95-th percentile0.0007335
Maximum0.01415
Range0.01365
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0004697395319
Coefficient of variation (CV)0.8430053407
Kurtosis669.0008032
Mean0.0005572201139
Median Absolute Deviation (MAD)0
Skewness23.65435824
Sum0.58731
Variance2.206552279 × 10-7
MonotonicityNot monotonic
2022-11-15T17:21:27.383253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0005966
91.7%
0.000613
 
1.2%
0.000864
 
0.4%
0.000513
 
0.3%
0.00083
 
0.3%
0.00072
 
0.2%
0.000652
 
0.2%
0.001012
 
0.2%
0.001282
 
0.2%
0.000642
 
0.2%
Other values (49)55
 
5.2%
ValueCountFrequency (%)
0.0005966
91.7%
0.000513
 
0.3%
0.000522
 
0.2%
0.000541
 
0.1%
0.000551
 
0.1%
0.000562
 
0.2%
0.000581
 
0.1%
0.000591
 
0.1%
0.000613
 
1.2%
0.000621
 
0.1%
ValueCountFrequency (%)
0.014151
0.1%
0.003141
0.1%
0.002891
0.1%
0.002541
0.1%
0.002161
0.1%
0.001891
0.1%
0.001851
0.1%
0.001841
0.1%
0.00171
0.1%
0.001681
0.1%

CALIDAD_HG
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
Potable - Excelente
1053 
No apta como FAAP
 
1

Length

Max length19
Median length19
Mean length18.99810247
Min length17

Characters and Unicode

Total characters20024
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowPotable - Excelente
2nd rowPotable - Excelente
3rd rowPotable - Excelente
4th rowPotable - Excelente
5th rowPotable - Excelente

Common Values

ValueCountFrequency (%)
Potable - Excelente1053
99.9%
No apta como FAAP1
 
0.1%

Length

2022-11-15T17:21:27.454514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:27.513412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
potable1053
33.3%
1053
33.3%
excelente1053
33.3%
no1
 
< 0.1%
apta1
 
< 0.1%
como1
 
< 0.1%
faap1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e4212
21.0%
2109
10.5%
t2107
10.5%
l2106
10.5%
o1056
 
5.3%
a1055
 
5.3%
P1054
 
5.3%
c1054
 
5.3%
x1053
 
5.3%
n1053
 
5.3%
Other values (8)3165
15.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14751
73.7%
Uppercase Letter2111
 
10.5%
Space Separator2109
 
10.5%
Dash Punctuation1053
 
5.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4212
28.6%
t2107
14.3%
l2106
14.3%
o1056
 
7.2%
a1055
 
7.2%
c1054
 
7.1%
x1053
 
7.1%
n1053
 
7.1%
b1053
 
7.1%
p1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
P1054
49.9%
E1053
49.9%
A2
 
0.1%
N1
 
< 0.1%
F1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
2109
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1053
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16862
84.2%
Common3162
 
15.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4212
25.0%
t2107
12.5%
l2106
12.5%
o1056
 
6.3%
a1055
 
6.3%
P1054
 
6.3%
c1054
 
6.3%
x1053
 
6.2%
n1053
 
6.2%
E1053
 
6.2%
Other values (6)1059
 
6.3%
Common
ValueCountFrequency (%)
2109
66.7%
-1053
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII20024
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4212
21.0%
2109
10.5%
t2107
10.5%
l2106
10.5%
o1056
 
5.3%
a1055
 
5.3%
P1054
 
5.3%
c1054
 
5.3%
x1053
 
5.3%
n1053
 
5.3%
Other values (8)3165
15.8%

PB_TOT_mg/L
Real number (ℝ≥0)

HIGH CORRELATION

Distinct30
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.005285313093
Minimum0.005
Maximum0.0809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2022-11-15T17:21:27.563487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.005
5-th percentile0.005
Q10.005
median0.005
Q30.005
95-th percentile0.005
Maximum0.0809
Range0.0759
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.003275771637
Coefficient of variation (CV)0.6197876226
Kurtosis333.9750808
Mean0.005285313093
Median Absolute Deviation (MAD)0
Skewness17.12295247
Sum5.57072
Variance1.073067982 × 10-5
MonotonicityNot monotonic
2022-11-15T17:21:27.624509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.0051025
97.2%
0.03991
 
0.1%
0.007091
 
0.1%
0.005961
 
0.1%
0.0461
 
0.1%
0.007441
 
0.1%
0.006441
 
0.1%
0.006191
 
0.1%
0.007031
 
0.1%
0.01331
 
0.1%
Other values (20)20
 
1.9%
ValueCountFrequency (%)
0.0051025
97.2%
0.00531
 
0.1%
0.005561
 
0.1%
0.005571
 
0.1%
0.005961
 
0.1%
0.006191
 
0.1%
0.006441
 
0.1%
0.007031
 
0.1%
0.007091
 
0.1%
0.007341
 
0.1%
ValueCountFrequency (%)
0.08091
0.1%
0.0491
0.1%
0.0461
0.1%
0.03991
0.1%
0.02191
0.1%
0.01521
0.1%
0.01351
0.1%
0.01331
0.1%
0.012251
0.1%
0.01161
0.1%

CALIDAD_PB
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
Potable - Excelente
1042 
No apta como FAAP
 
12

Length

Max length19
Median length19
Mean length18.9772296
Min length17

Characters and Unicode

Total characters20002
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPotable - Excelente
2nd rowPotable - Excelente
3rd rowPotable - Excelente
4th rowPotable - Excelente
5th rowPotable - Excelente

Common Values

ValueCountFrequency (%)
Potable - Excelente1042
98.9%
No apta como FAAP12
 
1.1%

Length

2022-11-15T17:21:27.690923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:27.751719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
potable1042
32.8%
1042
32.8%
excelente1042
32.8%
no12
 
0.4%
apta12
 
0.4%
como12
 
0.4%
faap12
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e4168
20.8%
2120
10.6%
t2096
10.5%
l2084
10.4%
o1078
 
5.4%
a1066
 
5.3%
P1054
 
5.3%
c1054
 
5.3%
x1042
 
5.2%
n1042
 
5.2%
Other values (8)3198
16.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14696
73.5%
Uppercase Letter2144
 
10.7%
Space Separator2120
 
10.6%
Dash Punctuation1042
 
5.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4168
28.4%
t2096
14.3%
l2084
14.2%
o1078
 
7.3%
a1066
 
7.3%
c1054
 
7.2%
x1042
 
7.1%
n1042
 
7.1%
b1042
 
7.1%
p12
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
P1054
49.2%
E1042
48.6%
A24
 
1.1%
N12
 
0.6%
F12
 
0.6%
Space Separator
ValueCountFrequency (%)
2120
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1042
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16840
84.2%
Common3162
 
15.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4168
24.8%
t2096
12.4%
l2084
12.4%
o1078
 
6.4%
a1066
 
6.3%
P1054
 
6.3%
c1054
 
6.3%
x1042
 
6.2%
n1042
 
6.2%
E1042
 
6.2%
Other values (6)1114
 
6.6%
Common
ValueCountFrequency (%)
2120
67.0%
-1042
33.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII20002
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4168
20.8%
2120
10.6%
t2096
10.5%
l2084
10.4%
o1078
 
5.4%
a1066
 
5.3%
P1054
 
5.3%
c1054
 
5.3%
x1042
 
5.2%
n1042
 
5.2%
Other values (8)3198
16.0%

MN_TOT_mg/L
Real number (ℝ≥0)

HIGH CORRELATION

Distinct357
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07295989564
Minimum0.0015
Maximum8.982
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2022-11-15T17:21:27.810403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0015
5-th percentile0.0015
Q10.0015
median0.0015
Q30.00983
95-th percentile0.365793
Maximum8.982
Range8.9805
Interquartile range (IQR)0.00833

Descriptive statistics

Standard deviation0.3788564946
Coefficient of variation (CV)5.192667715
Kurtosis303.7800881
Mean0.07295989564
Median Absolute Deviation (MAD)0
Skewness14.62076602
Sum76.89973
Variance0.1435322435
MonotonicityNot monotonic
2022-11-15T17:21:27.879433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0015548
52.0%
0.001712
 
1.1%
0.002110
 
0.9%
0.00169
 
0.9%
0.0038
 
0.8%
0.00237
 
0.7%
0.00227
 
0.7%
0.00197
 
0.7%
0.00286
 
0.6%
0.0026
 
0.6%
Other values (347)434
41.2%
ValueCountFrequency (%)
0.0015548
52.0%
0.00169
 
0.9%
0.001631
 
0.1%
0.001691
 
0.1%
0.001712
 
1.1%
0.001771
 
0.1%
0.001791
 
0.1%
0.00184
 
0.4%
0.001871
 
0.1%
0.001891
 
0.1%
ValueCountFrequency (%)
8.9821
0.1%
3.45481
0.1%
3.2791
0.1%
2.0151
0.1%
1.961
0.1%
1.7711
0.1%
1.61
0.1%
1.5681
0.1%
1.5571
0.1%
1.4771
0.1%

CALIDAD_MN
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
Potable - Excelente
969 
Puede afectar la salud
 
50
Sin efectos en la salud - Puede dar color al agua
 
35

Length

Max length49
Median length19
Mean length20.13851992
Min length19

Characters and Unicode

Total characters21226
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPotable - Excelente
2nd rowPotable - Excelente
3rd rowPotable - Excelente
4th rowPotable - Excelente
5th rowPotable - Excelente

Common Values

ValueCountFrequency (%)
Potable - Excelente969
91.9%
Puede afectar la salud50
 
4.7%
Sin efectos en la salud - Puede dar color al agua35
 
3.3%

Length

2022-11-15T17:21:27.942829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:27.999994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1004
28.8%
potable969
27.7%
excelente969
27.7%
puede85
 
2.4%
la85
 
2.4%
salud85
 
2.4%
afectar50
 
1.4%
sin35
 
1.0%
efectos35
 
1.0%
en35
 
1.0%
Other values (4)140
 
4.0%

Most occurring characters

ValueCountFrequency (%)
e4201
19.8%
2438
11.5%
l2178
10.3%
t2023
9.5%
a1379
 
6.5%
c1089
 
5.1%
o1074
 
5.1%
P1054
 
5.0%
n1039
 
4.9%
-1004
 
4.7%
Other values (11)3747
17.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15726
74.1%
Space Separator2438
 
11.5%
Uppercase Letter2058
 
9.7%
Dash Punctuation1004
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4201
26.7%
l2178
13.8%
t2023
12.9%
a1379
 
8.8%
c1089
 
6.9%
o1074
 
6.8%
n1039
 
6.6%
x969
 
6.2%
b969
 
6.2%
u205
 
1.3%
Other values (6)600
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
P1054
51.2%
E969
47.1%
S35
 
1.7%
Space Separator
ValueCountFrequency (%)
2438
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1004
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17784
83.8%
Common3442
 
16.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4201
23.6%
l2178
12.2%
t2023
11.4%
a1379
 
7.8%
c1089
 
6.1%
o1074
 
6.0%
P1054
 
5.9%
n1039
 
5.8%
x969
 
5.4%
E969
 
5.4%
Other values (9)1809
10.2%
Common
ValueCountFrequency (%)
2438
70.8%
-1004
29.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII21226
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4201
19.8%
2438
11.5%
l2178
10.3%
t2023
9.5%
a1379
 
6.5%
c1089
 
5.1%
o1074
 
5.1%
P1054
 
5.0%
n1039
 
4.9%
-1004
 
4.7%
Other values (11)3747
17.7%

FE_TOT_mg/L
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct606
Distinct (%)57.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4122343928
Minimum0.025
Maximum178.615
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2022-11-15T17:21:28.060601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.025
5-th percentile0.025
Q10.025
median0.0469
Q30.172275
95-th percentile0.908067
Maximum178.615
Range178.59
Interquartile range (IQR)0.147275

Descriptive statistics

Standard deviation5.574306689
Coefficient of variation (CV)13.52217764
Kurtosis994.6876848
Mean0.4122343928
Median Absolute Deviation (MAD)0.0219
Skewness31.13993832
Sum434.49505
Variance31.07289507
MonotonicityNot monotonic
2022-11-15T17:21:28.130796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.025396
37.6%
0.02884
 
0.4%
0.04924
 
0.4%
0.05643
 
0.3%
0.04833
 
0.3%
0.04713
 
0.3%
0.03463
 
0.3%
0.20082
 
0.2%
0.04372
 
0.2%
0.03042
 
0.2%
Other values (596)632
60.0%
ValueCountFrequency (%)
0.025396
37.6%
0.02532
 
0.2%
0.02561
 
0.1%
0.02571
 
0.1%
0.02591
 
0.1%
0.0261
 
0.1%
0.02611
 
0.1%
0.02661
 
0.1%
0.027011
 
0.1%
0.027121
 
0.1%
ValueCountFrequency (%)
178.6151
0.1%
16.43711
0.1%
14.061
0.1%
13.441
0.1%
7.3821
0.1%
5.2581
0.1%
5.0171
0.1%
4.651
0.1%
4.4631
0.1%
4.2971
0.1%

CALIDAD_FE
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
Potable - Excelente
920 
Sin efectos en la salud - Puede dar color al agua
134 

Length

Max length49
Median length19
Mean length22.81404175
Min length19

Characters and Unicode

Total characters24046
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPotable - Excelente
2nd rowPotable - Excelente
3rd rowPotable - Excelente
4th rowPotable - Excelente
5th rowPotable - Excelente

Common Values

ValueCountFrequency (%)
Potable - Excelente920
87.3%
Sin efectos en la salud - Puede dar color al agua134
 
12.7%

Length

2022-11-15T17:21:28.196978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:28.257725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1054
24.9%
potable920
21.7%
excelente920
21.7%
sin134
 
3.2%
efectos134
 
3.2%
en134
 
3.2%
la134
 
3.2%
salud134
 
3.2%
puede134
 
3.2%
dar134
 
3.2%
Other values (3)402
 
9.5%

Most occurring characters

ValueCountFrequency (%)
e4350
18.1%
3180
13.2%
l2376
9.9%
t1974
8.2%
a1724
 
7.2%
o1322
 
5.5%
n1188
 
4.9%
c1188
 
4.9%
P1054
 
4.4%
-1054
 
4.4%
Other values (11)4636
19.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter17704
73.6%
Space Separator3180
 
13.2%
Uppercase Letter2108
 
8.8%
Dash Punctuation1054
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4350
24.6%
l2376
13.4%
t1974
11.2%
a1724
 
9.7%
o1322
 
7.5%
n1188
 
6.7%
c1188
 
6.7%
x920
 
5.2%
b920
 
5.2%
u402
 
2.3%
Other values (6)1340
 
7.6%
Uppercase Letter
ValueCountFrequency (%)
P1054
50.0%
E920
43.6%
S134
 
6.4%
Space Separator
ValueCountFrequency (%)
3180
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1054
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin19812
82.4%
Common4234
 
17.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4350
22.0%
l2376
12.0%
t1974
10.0%
a1724
 
8.7%
o1322
 
6.7%
n1188
 
6.0%
c1188
 
6.0%
P1054
 
5.3%
x920
 
4.6%
E920
 
4.6%
Other values (9)2796
14.1%
Common
ValueCountFrequency (%)
3180
75.1%
-1054
 
24.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII24046
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4350
18.1%
3180
13.2%
l2376
9.9%
t1974
8.2%
a1724
 
7.2%
o1322
 
5.5%
n1188
 
4.9%
c1188
 
4.9%
P1054
 
4.4%
-1054
 
4.4%
Other values (11)4636
19.3%

SEMAFORO
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
Verde
427 
Rojo
382 
Amarillo
245 

Length

Max length8
Median length5
Mean length5.334914611
Min length4

Characters and Unicode

Total characters5623
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVerde
2nd rowVerde
3rd rowRojo
4th rowVerde
5th rowRojo

Common Values

ValueCountFrequency (%)
Verde427
40.5%
Rojo382
36.2%
Amarillo245
23.2%

Length

2022-11-15T17:21:28.314373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:28.377714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
verde427
40.5%
rojo382
36.2%
amarillo245
23.2%

Most occurring characters

ValueCountFrequency (%)
o1009
17.9%
e854
15.2%
r672
12.0%
l490
8.7%
V427
7.6%
d427
7.6%
R382
 
6.8%
j382
 
6.8%
A245
 
4.4%
m245
 
4.4%
Other values (2)490
8.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4569
81.3%
Uppercase Letter1054
 
18.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o1009
22.1%
e854
18.7%
r672
14.7%
l490
10.7%
d427
9.3%
j382
 
8.4%
m245
 
5.4%
a245
 
5.4%
i245
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
V427
40.5%
R382
36.2%
A245
23.2%

Most occurring scripts

ValueCountFrequency (%)
Latin5623
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o1009
17.9%
e854
15.2%
r672
12.0%
l490
8.7%
V427
7.6%
d427
7.6%
R382
 
6.8%
j382
 
6.8%
A245
 
4.4%
m245
 
4.4%
Other values (2)490
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII5623
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o1009
17.9%
e854
15.2%
r672
12.0%
l490
8.7%
V427
7.6%
d427
7.6%
R382
 
6.8%
j382
 
6.8%
A245
 
4.4%
m245
 
4.4%
Other values (2)490
8.7%

CUMPLE_CON_ALC
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
SI
997 
NO
 
57

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2108
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSI
2nd rowSI
3rd rowSI
4th rowSI
5th rowSI

Common Values

ValueCountFrequency (%)
SI997
94.6%
NO57
 
5.4%

Length

2022-11-15T17:21:28.427346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:28.478891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
si997
94.6%
no57
 
5.4%

Most occurring characters

ValueCountFrequency (%)
S997
47.3%
I997
47.3%
N57
 
2.7%
O57
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2108
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S997
47.3%
I997
47.3%
N57
 
2.7%
O57
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Latin2108
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S997
47.3%
I997
47.3%
N57
 
2.7%
O57
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S997
47.3%
I997
47.3%
N57
 
2.7%
O57
 
2.7%

CUMPLE_CON_COND
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
SI
931 
NO
123 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2108
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSI
2nd rowSI
3rd rowSI
4th rowSI
5th rowSI

Common Values

ValueCountFrequency (%)
SI931
88.3%
NO123
 
11.7%

Length

2022-11-15T17:21:28.522478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:28.574723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
si931
88.3%
no123
 
11.7%

Most occurring characters

ValueCountFrequency (%)
S931
44.2%
I931
44.2%
N123
 
5.8%
O123
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2108
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S931
44.2%
I931
44.2%
N123
 
5.8%
O123
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Latin2108
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S931
44.2%
I931
44.2%
N123
 
5.8%
O123
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII2108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S931
44.2%
I931
44.2%
N123
 
5.8%
O123
 
5.8%

CUMPLE_CON_SDT_ra
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
SI
984 
NO
 
70

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2108
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSI
2nd rowSI
3rd rowSI
4th rowSI
5th rowSI

Common Values

ValueCountFrequency (%)
SI984
93.4%
NO70
 
6.6%

Length

2022-11-15T17:21:28.620151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:28.672176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
si984
93.4%
no70
 
6.6%

Most occurring characters

ValueCountFrequency (%)
S984
46.7%
I984
46.7%
N70
 
3.3%
O70
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2108
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S984
46.7%
I984
46.7%
N70
 
3.3%
O70
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Latin2108
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S984
46.7%
I984
46.7%
N70
 
3.3%
O70
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S984
46.7%
I984
46.7%
N70
 
3.3%
O70
 
3.3%

CUMPLE_CON_SDT_salin
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
SI
984 
NO
 
70

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2108
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSI
2nd rowSI
3rd rowSI
4th rowSI
5th rowSI

Common Values

ValueCountFrequency (%)
SI984
93.4%
NO70
 
6.6%

Length

2022-11-15T17:21:28.716291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:28.874910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
si984
93.4%
no70
 
6.6%

Most occurring characters

ValueCountFrequency (%)
S984
46.7%
I984
46.7%
N70
 
3.3%
O70
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2108
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S984
46.7%
I984
46.7%
N70
 
3.3%
O70
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Latin2108
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S984
46.7%
I984
46.7%
N70
 
3.3%
O70
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S984
46.7%
I984
46.7%
N70
 
3.3%
O70
 
3.3%

CUMPLE_CON_FLUO
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
SI
864 
NO
190 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2108
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSI
2nd rowSI
3rd rowNO
4th rowSI
5th rowSI

Common Values

ValueCountFrequency (%)
SI864
82.0%
NO190
 
18.0%

Length

2022-11-15T17:21:28.918755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:28.971021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
si864
82.0%
no190
 
18.0%

Most occurring characters

ValueCountFrequency (%)
S864
41.0%
I864
41.0%
N190
 
9.0%
O190
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2108
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S864
41.0%
I864
41.0%
N190
 
9.0%
O190
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2108
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S864
41.0%
I864
41.0%
N190
 
9.0%
O190
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S864
41.0%
I864
41.0%
N190
 
9.0%
O190
 
9.0%

CUMPLE_CON_DUR
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
SI
829 
NO
225 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2108
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSI
2nd rowSI
3rd rowSI
4th rowSI
5th rowSI

Common Values

ValueCountFrequency (%)
SI829
78.7%
NO225
 
21.3%

Length

2022-11-15T17:21:29.016211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:29.068374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
si829
78.7%
no225
 
21.3%

Most occurring characters

ValueCountFrequency (%)
S829
39.3%
I829
39.3%
N225
 
10.7%
O225
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2108
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S829
39.3%
I829
39.3%
N225
 
10.7%
O225
 
10.7%

Most occurring scripts

ValueCountFrequency (%)
Latin2108
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S829
39.3%
I829
39.3%
N225
 
10.7%
O225
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S829
39.3%
I829
39.3%
N225
 
10.7%
O225
 
10.7%

CUMPLE_CON_CF
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
SI
993 
NO
 
61

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2108
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSI
2nd rowSI
3rd rowSI
4th rowSI
5th rowSI

Common Values

ValueCountFrequency (%)
SI993
94.2%
NO61
 
5.8%

Length

2022-11-15T17:21:29.113602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:29.165065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
si993
94.2%
no61
 
5.8%

Most occurring characters

ValueCountFrequency (%)
S993
47.1%
I993
47.1%
N61
 
2.9%
O61
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2108
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S993
47.1%
I993
47.1%
N61
 
2.9%
O61
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Latin2108
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S993
47.1%
I993
47.1%
N61
 
2.9%
O61
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S993
47.1%
I993
47.1%
N61
 
2.9%
O61
 
2.9%

CUMPLE_CON_NO3
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
SI
974 
NO
 
80

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2108
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSI
2nd rowSI
3rd rowSI
4th rowSI
5th rowNO

Common Values

ValueCountFrequency (%)
SI974
92.4%
NO80
 
7.6%

Length

2022-11-15T17:21:29.208976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:29.260889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
si974
92.4%
no80
 
7.6%

Most occurring characters

ValueCountFrequency (%)
S974
46.2%
I974
46.2%
N80
 
3.8%
O80
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2108
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S974
46.2%
I974
46.2%
N80
 
3.8%
O80
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin2108
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S974
46.2%
I974
46.2%
N80
 
3.8%
O80
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII2108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S974
46.2%
I974
46.2%
N80
 
3.8%
O80
 
3.8%

CUMPLE_CON_AS
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
SI
929 
NO
125 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2108
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSI
2nd rowSI
3rd rowNO
4th rowSI
5th rowSI

Common Values

ValueCountFrequency (%)
SI929
88.1%
NO125
 
11.9%

Length

2022-11-15T17:21:29.304718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:29.357007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
si929
88.1%
no125
 
11.9%

Most occurring characters

ValueCountFrequency (%)
S929
44.1%
I929
44.1%
N125
 
5.9%
O125
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2108
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S929
44.1%
I929
44.1%
N125
 
5.9%
O125
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Latin2108
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S929
44.1%
I929
44.1%
N125
 
5.9%
O125
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S929
44.1%
I929
44.1%
N125
 
5.9%
O125
 
5.9%

CUMPLE_CON_CD
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
SI
1052 
NO
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2108
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSI
2nd rowSI
3rd rowSI
4th rowSI
5th rowSI

Common Values

ValueCountFrequency (%)
SI1052
99.8%
NO2
 
0.2%

Length

2022-11-15T17:21:29.403041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:29.455258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
si1052
99.8%
no2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
S1052
49.9%
I1052
49.9%
N2
 
0.1%
O2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2108
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S1052
49.9%
I1052
49.9%
N2
 
0.1%
O2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin2108
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S1052
49.9%
I1052
49.9%
N2
 
0.1%
O2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S1052
49.9%
I1052
49.9%
N2
 
0.1%
O2
 
0.1%

CUMPLE_CON_CR
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
SI
1039 
NO
 
15

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2108
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSI
2nd rowSI
3rd rowSI
4th rowSI
5th rowSI

Common Values

ValueCountFrequency (%)
SI1039
98.6%
NO15
 
1.4%

Length

2022-11-15T17:21:29.498798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:29.550051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
si1039
98.6%
no15
 
1.4%

Most occurring characters

ValueCountFrequency (%)
S1039
49.3%
I1039
49.3%
N15
 
0.7%
O15
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2108
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S1039
49.3%
I1039
49.3%
N15
 
0.7%
O15
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Latin2108
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S1039
49.3%
I1039
49.3%
N15
 
0.7%
O15
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S1039
49.3%
I1039
49.3%
N15
 
0.7%
O15
 
0.7%

CUMPLE_CON_HG
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
SI
1053 
NO
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2108
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowSI
2nd rowSI
3rd rowSI
4th rowSI
5th rowSI

Common Values

ValueCountFrequency (%)
SI1053
99.9%
NO1
 
0.1%

Length

2022-11-15T17:21:29.594045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:29.645564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
si1053
99.9%
no1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S1053
50.0%
I1053
50.0%
N1
 
< 0.1%
O1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2108
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S1053
50.0%
I1053
50.0%
N1
 
< 0.1%
O1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin2108
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S1053
50.0%
I1053
50.0%
N1
 
< 0.1%
O1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S1053
50.0%
I1053
50.0%
N1
 
< 0.1%
O1
 
< 0.1%

CUMPLE_CON_PB
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
SI
1042 
NO
 
12

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2108
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSI
2nd rowSI
3rd rowSI
4th rowSI
5th rowSI

Common Values

ValueCountFrequency (%)
SI1042
98.9%
NO12
 
1.1%

Length

2022-11-15T17:21:29.689328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:29.742048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
si1042
98.9%
no12
 
1.1%

Most occurring characters

ValueCountFrequency (%)
S1042
49.4%
I1042
49.4%
N12
 
0.6%
O12
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2108
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S1042
49.4%
I1042
49.4%
N12
 
0.6%
O12
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin2108
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S1042
49.4%
I1042
49.4%
N12
 
0.6%
O12
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S1042
49.4%
I1042
49.4%
N12
 
0.6%
O12
 
0.6%

CUMPLE_CON_MN
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
SI
969 
NO
 
85

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2108
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSI
2nd rowSI
3rd rowSI
4th rowSI
5th rowSI

Common Values

ValueCountFrequency (%)
SI969
91.9%
NO85
 
8.1%

Length

2022-11-15T17:21:29.786157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:29.838298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
si969
91.9%
no85
 
8.1%

Most occurring characters

ValueCountFrequency (%)
S969
46.0%
I969
46.0%
N85
 
4.0%
O85
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2108
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S969
46.0%
I969
46.0%
N85
 
4.0%
O85
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2108
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S969
46.0%
I969
46.0%
N85
 
4.0%
O85
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S969
46.0%
I969
46.0%
N85
 
4.0%
O85
 
4.0%

CUMPLE_CON_FE
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.4 KiB
SI
920 
NO
134 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2108
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSI
2nd rowSI
3rd rowSI
4th rowSI
5th rowSI

Common Values

ValueCountFrequency (%)
SI920
87.3%
NO134
 
12.7%

Length

2022-11-15T17:21:29.883628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-15T17:21:29.935946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
si920
87.3%
no134
 
12.7%

Most occurring characters

ValueCountFrequency (%)
S920
43.6%
I920
43.6%
N134
 
6.4%
O134
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2108
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S920
43.6%
I920
43.6%
N134
 
6.4%
O134
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Latin2108
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S920
43.6%
I920
43.6%
N134
 
6.4%
O134
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S920
43.6%
I920
43.6%
N134
 
6.4%
O134
 
6.4%

Interactions

2022-11-15T17:21:21.455779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:05.343069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:06.451838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:07.461954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:08.609512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:09.683985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:10.875156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:11.864575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:12.998728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:14.043917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:15.145621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:16.149815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:17.224867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:18.205445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:19.355646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:20.396192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:21.516455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:05.410661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:06.513198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:07.525365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:08.672858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:09.749104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:10.933457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:11.927351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:13.061742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:14.104157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:15.205963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:16.209345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:17.283860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:18.268477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:19.418839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:20.453667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:21.579358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:05.575301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:06.574313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:07.589202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:08.737068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:09.816008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:10.993241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:11.989688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:13.127628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:14.165401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:15.267561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:16.268656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:17.344970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:18.332592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:19.481799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:20.512182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:21.646081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:05.640060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:06.639730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:07.656644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:08.809937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:09.885057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:11.057309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:12.056220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:13.195076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:14.230260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:15.332756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:16.332238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:17.408946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:18.401823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:19.549087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:20.574449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:21.713504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:05.705442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:06.706734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:07.830154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:08.877841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:10.062235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:11.121110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:12.125609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:13.262637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:14.295307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:15.397830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:16.395824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:17.472603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:18.470252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:19.616322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:20.637460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:21.779825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:05.770556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:06.771583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:07.896845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:08.944947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:10.131859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:11.184947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:12.192793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:13.329887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:14.359885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:15.462489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:16.458418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:17.535805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:18.537863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:19.682981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:20.699436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:21.839368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:05.828672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:06.830395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:07.957183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:09.008512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:10.196552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:11.241798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:12.355928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:13.391525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:14.520140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:15.521304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:16.515296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:17.593196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:18.599152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:19.743095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:20.754852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:21.905285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:05.892366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:06.895874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:08.023489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:09.079306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:10.266985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:11.304771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:12.422059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:13.458022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:14.583617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:15.586036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:16.577034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:17.656129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:18.665315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:19.809240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:20.816138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:21.972140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:05.958040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:06.961526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:08.092433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:09.151729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:10.340556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:11.370720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:12.490092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:13.526261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:14.648802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:15.652257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:16.741253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:17.720030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:18.733040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:19.876907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:20.880122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:22.033874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:06.019294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:07.024510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:08.156096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:09.219448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:10.408110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:11.432166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:12.553667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:13.590430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:14.709751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:15.714086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:16.801190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:17.779415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:18.798080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:19.939924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:20.938709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:22.097646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:06.081524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:07.086907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:08.219417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:09.286756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:10.474579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:11.492462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:12.616877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:13.654332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:14.771922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:15.775815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:16.861183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:17.840104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:18.965836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:20.004100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:20.997792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:22.157903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:06.140317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:07.146341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:08.281558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:09.352202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:10.539290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:11.550221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:12.678755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:13.716527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:14.831058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:15.834976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:16.918171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:17.897741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:19.027843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:20.065805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:21.156519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:22.219277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:06.199989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:07.208329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:08.343216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:09.417069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:10.604733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:11.610530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:12.740430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:13.779374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:14.891432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:15.894756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:16.976460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:17.955977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:19.090289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:20.130620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:21.213545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:22.286754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:06.265161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:07.273448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:08.411098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:09.487394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:10.675758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:11.674634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:12.807806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:13.847730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:14.957462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:15.960562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:17.040418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:18.020814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:19.158483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:20.199612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:21.276411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:22.352928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:06.330361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:07.339117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:08.480403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:09.556319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:10.746195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:11.738968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:12.874804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:13.916156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:15.023211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:16.026222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:17.107082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:18.085572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:19.227019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:20.268266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:21.338826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:22.412179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:06.388595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:07.397758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:08.543213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:09.617920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:10.808608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:11.799613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:12.934304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:13.977180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:15.082011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:16.084975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:17.163341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:18.142883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:19.288786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:20.330164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-15T17:21:21.394360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-11-15T17:21:30.018272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-15T17:21:30.499436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-15T17:21:30.625346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-15T17:21:30.751004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-15T17:21:30.891155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-15T17:21:31.207105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-15T17:21:22.628778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexCLAVESITIOORGANISMO_DE_CUENCAESTADOMUNICIPIOACUIFEROSUBTIPOLONGITUDLATITUDPERIODOALC_mg/LCALIDAD_ALCCONDUCT_mS/cmCALIDAD_CONDUCSDT_M_mg/LCALIDAD_SDT_raCALIDAD_SDT_salinFLUORUROS_mg/LCALIDAD_FLUODUR_mg/LCALIDAD_DURCOLI_FEC_NMP/100_mLCALIDAD_COLI_FECN_NO3_mg/LCALIDAD_N_NO3AS_TOT_mg/LCALIDAD_ASCD_TOT_mg/LCALIDAD_CDCR_TOT_mg/LCALIDAD_CRHG_TOT_mg/LCALIDAD_HGPB_TOT_mg/LCALIDAD_PBMN_TOT_mg/LCALIDAD_MNFE_TOT_mg/LCALIDAD_FESEMAFOROCUMPLE_CON_ALCCUMPLE_CON_CONDCUMPLE_CON_SDT_raCUMPLE_CON_SDT_salinCUMPLE_CON_FLUOCUMPLE_CON_DURCUMPLE_CON_CFCUMPLE_CON_NO3CUMPLE_CON_ASCUMPLE_CON_CDCUMPLE_CON_CRCUMPLE_CON_HGCUMPLE_CON_PBCUMPLE_CON_MNCUMPLE_CON_FE
00DLAGU6POZO SAN GILLERMA SANTIAGO PACIFICOAGUASCALIENTESASIENTOSVALLE DE CHICALOTEPOZO-102.02210022.2088702020.0229.990Alta940.0Permisible para riego603.6Cultivos sensiblesPotable - Dulce0.9766Potable - Optima213.7320Potable - Dura1.1Potable - Excelente4.184656Potable - Excelente0.0161Apta como FAAP0.003Potable - Excelente0.005Potable - Excelente0.0005Potable - Excelente0.005Potable - Excelente0.0015Potable - Excelente0.0891Potable - ExcelenteVerdeSISISISISISISISISISISISISISISI
11DLAGU6516POZO R013 CAÑADA HONDALERMA SANTIAGO PACIFICOAGUASCALIENTESAGUASCALIENTESVALLE DE CHICALOTEPOZO-102.20075021.9995802020.0231.990Alta608.0Buena para riego445.4Excelente para riegoPotable - Dulce0.9298Potable - Optima185.0514Potable - Dura1.1Potable - Excelente5.750110Potable - Buena calidad0.0134Apta como FAAP0.003Potable - Excelente0.005Potable - Excelente0.0005Potable - Excelente0.005Potable - Excelente0.0015Potable - Excelente0.0250Potable - ExcelenteVerdeSISISISISISISISISISISISISISISI
22DLAGU7POZO COSIOLERMA SANTIAGO PACIFICOAGUASCALIENTESCOSIOVALLE DE AGUASCALIENTESPOZO-102.28801022.3668502020.0204.920Alta532.0Buena para riego342.0Excelente para riegoPotable - Dulce1.8045Alta120.7190Potable - Dura1.1Potable - Excelente1.449803Potable - Excelente0.0370No apta como FAAP0.003Potable - Excelente0.005Potable - Excelente0.0005Potable - Excelente0.005Potable - Excelente0.0015Potable - Excelente0.0250Potable - ExcelenteRojoSISISISINOSISISINOSISISISISISI
33DLAGU9POZO EL SALITRILLOLERMA SANTIAGO PACIFICOAGUASCALIENTESRINCON DE ROMOSVALLE DE AGUASCALIENTESPOZO-102.29449022.1843502020.0327.000Alta686.0Buena para riego478.6Excelente para riegoPotable - Dulce1.1229Potable - Optima199.8790Potable - Dura1.1Potable - Excelente1.258597Potable - Excelente0.0154Apta como FAAP0.003Potable - Excelente0.005Potable - Excelente0.0005Potable - Excelente0.005Potable - Excelente0.0015Potable - Excelente0.0250Potable - ExcelenteVerdeSISISISISISISISISISISISISISISI
44DLBAJ107RANCHO EL TECOLOTEPENINSULA DE BAJA CALIFORNIABAJA CALIFORNIA SURLA PAZTODOS SANTOSPOZO-110.24480023.4513802020.0309.885Alta1841.0Permisible para riego1179.0Cultivos con manejo especialLigeramente salobres0.2343Baja476.9872Potable - Dura291.0Aceptable15.672251No apta como FAAP0.0100Potable - Excelente0.003Potable - Excelente0.005Potable - Excelente0.0005Potable - Excelente0.005Potable - Excelente0.0015Potable - Excelente0.0250Potable - ExcelenteRojoSISISISISISISINOSISISISISISISI
55DLBAJ108POZO A.P. CNA 7 (ANTES POZO A.P. CNA 6)PENINSULA DE BAJA CALIFORNIABAJA CALIFORNIA SURLA PAZTODOS SANTOSPOZO-110.22067023.4649302020.0224.475Alta570.3Buena para riego554.8Cultivos sensiblesPotable - Dulce0.2756Baja201.8784Potable - Dura6131.0Contaminada8.555740Potable - Buena calidad0.0100Potable - Excelente0.003Potable - Excelente0.005Potable - Excelente0.0005Potable - Excelente0.005Potable - Excelente0.0015Potable - Excelente0.0250Potable - ExcelenteRojoSISISISISISINOSISISISISISISISI
66DLBAJ110POZO 26, SAN JUANPENINSULA DE BAJA CALIFORNIABAJA CALIFORNIA SURLA PAZTODOS SANTOSPOZO-110.21396023.4746002020.0203.670Alta531.0Buena para riego278.8Excelente para riegoPotable - Dulce0.2890Baja166.2528Potable - Dura110.0Buena calidad4.686470Potable - Excelente0.0100Potable - Excelente0.003Potable - Excelente0.005Potable - Excelente0.0005Potable - Excelente0.005Potable - Excelente0.0015Potable - Excelente0.0250Potable - ExcelenteVerdeSISISISISISISISISISISISISISISI
77DLBAJ111VICTOR HUGO CESEÑAPENINSULA DE BAJA CALIFORNIABAJA CALIFORNIA SURLOS CABOSCABO SAN LUCASPOZO-109.90730622.8905002020.0350.760Alta2253.3Dudosa para riego1160.2Cultivos con manejo especialLigeramente salobres0.5607Media269.1712Potable - Dura798.0Aceptable27.600998No apta como FAAP0.0100Potable - Excelente0.003Potable - Excelente0.005Potable - Excelente0.0005Potable - Excelente0.005Potable - Excelente0.0041Potable - Excelente0.0692Potable - ExcelenteRojoSINOSISISISISINOSISISISISISISI
88DLBAJ117LAS PARRITASPENINSULA DE BAJA CALIFORNIABAJA CALIFORNIA SURLA PAZEL CARRIZALPOZO-110.08877823.7998612020.0343.655Alta1114.0Permisible para riego672.0Cultivos sensiblesPotable - Dulce0.3421Baja403.8482Potable - Dura146.0Buena calidad1.877325Potable - Excelente0.0100Potable - Excelente0.003Potable - Excelente0.005Potable - Excelente0.0005Potable - Excelente0.005Potable - Excelente0.0113Potable - Excelente0.1615Potable - ExcelenteVerdeSISISISISISISISISISISISISISISI
99DLBAJ118SAN ANTONIOPENINSULA DE BAJA CALIFORNIABAJA CALIFORNIA SURLA PAZLOS PLANESPOZO-110.05472223.8247222020.0332.605Alta1703.0Permisible para riego1017.8Cultivos con manejo especialLigeramente salobres0.5088Media559.0214Muy dura e indeseable usos industrial y domestico3873.0Contaminada0.143061Potable - Excelente0.3558No apta como FAAP0.003Potable - Excelente0.005Potable - Excelente0.0005Potable - Excelente0.005Potable - Excelente1.1670Puede afectar la salud14.0600Sin efectos en la salud - Puede dar color al aguaRojoSISISISISINONOSINOSISISISINONO

Last rows

df_indexCLAVESITIOORGANISMO_DE_CUENCAESTADOMUNICIPIOACUIFEROSUBTIPOLONGITUDLATITUDPERIODOALC_mg/LCALIDAD_ALCCONDUCT_mS/cmCALIDAD_CONDUCSDT_M_mg/LCALIDAD_SDT_raCALIDAD_SDT_salinFLUORUROS_mg/LCALIDAD_FLUODUR_mg/LCALIDAD_DURCOLI_FEC_NMP/100_mLCALIDAD_COLI_FECN_NO3_mg/LCALIDAD_N_NO3AS_TOT_mg/LCALIDAD_ASCD_TOT_mg/LCALIDAD_CDCR_TOT_mg/LCALIDAD_CRHG_TOT_mg/LCALIDAD_HGPB_TOT_mg/LCALIDAD_PBMN_TOT_mg/LCALIDAD_MNFE_TOT_mg/LCALIDAD_FESEMAFOROCUMPLE_CON_ALCCUMPLE_CON_CONDCUMPLE_CON_SDT_raCUMPLE_CON_SDT_salinCUMPLE_CON_FLUOCUMPLE_CON_DURCUMPLE_CON_CFCUMPLE_CON_NO3CUMPLE_CON_ASCUMPLE_CON_CDCUMPLE_CON_CRCUMPLE_CON_HGCUMPLE_CON_PBCUMPLE_CON_MNCUMPLE_CON_FE
10441058OCRBR5020M1POZO HOSPITAL CIVIL II (ROTONDA) MITRAS MONTERREYRIO BRAVONUEVO LEONMONTERREYAREA METROPOLITANA DE MONTERREYPOZO-100.3471525.687662020.0269.370Alta927.0Permisible para riego846.0000Cultivos sensiblesPotable - Dulce0.2644Baja395.8400Potable - Dura1.1Potable - Excelente12.758907No apta como FAAP0.01Potable - Excelente0.003Potable - Excelente0.005Potable - Excelente0.0005Potable - Excelente0.005Potable - Excelente0.00150Potable - Excelente0.02500Potable - ExcelenteRojoSISISISISISISINOSISISISISISISI
10451059OCRBR5046M2EJIDO EL CALVARIO (POZO COMUNITARIO)RIO BRAVONUEVO LEONCADEREYTA JIMENEZCITRICOLA NORTEPOZO-99.8381025.557592020.0374.490Alta1225.0Permisible para riego655.0000Cultivos sensiblesPotable - Dulce0.7671Potable - Optima351.0496Potable - Dura20.0Buena calidad9.101861Potable - Buena calidad0.01Potable - Excelente0.003Potable - Excelente0.005Potable - Excelente0.0005Potable - Excelente0.005Potable - Excelente0.00730Potable - Excelente0.02500Potable - ExcelenteVerdeSISISISISISISISISISISISISISISI
10461060OCRBR5087M1POZO RANCHO NOGALITOS-RAYMUNDO TREVIÑO-EJ. LA LAGUNA (RED DE REFERENCIA)RIO BRAVONUEVO LEONGENERAL TERANCITRICOLA NORTEPOZO-99.7294425.284442020.0323.000Alta1249.0Permisible para riego905.0000Cultivos sensiblesPotable - Dulce0.2054Baja622.0000Muy dura e indeseable usos industrial y domestico2400.0Contaminada8.163000Potable - Buena calidad0.01Potable - Excelente0.003Potable - Excelente0.005Potable - Excelente0.0005Potable - Excelente0.005Potable - Excelente0.01008Potable - Excelente0.08831Potable - ExcelenteRojoSISISISISINONOSISISISISISISISI
10471061OCRBR5093M1L-343 (EJIDO ELDIEZ)RIO BRAVONUEVO LEONLINARESCITRICOLA SURPOZO-99.4344124.848212020.0410.625Indeseable como FAAP1767.0Permisible para riego1011.4000Cultivos con manejo especialLigeramente salobres0.4368Media573.9680Muy dura e indeseable usos industrial y domestico1.1Potable - Excelente25.168563No apta como FAAP0.01Potable - Excelente0.003Potable - Excelente0.005Potable - Excelente0.0005Potable - Excelente0.005Potable - Excelente0.00150Potable - Excelente0.02500Potable - ExcelenteRojoNOSISISISINOSINOSISISISISISISI
10481062OCRBR5098M1L-363 - B (COMUNIDAD EL CARMEN DE LOS ELIZONDO)RIO BRAVONUEVO LEONLINARESCITRICOLA SURPOZO-99.4391424.974782020.0199.800Alta1622.0Permisible para riego1092.2000Cultivos con manejo especialLigeramente salobres0.4820Media679.2720Muy dura e indeseable usos industrial y domestico1.1Potable - Excelente2.357554Potable - Excelente0.01Potable - Excelente0.003Potable - Excelente0.005Potable - Excelente0.0005Potable - Excelente0.005Potable - Excelente0.00150Potable - Excelente0.02500Potable - ExcelenteAmarilloSISISISISINOSISISISISISISISISI
10491063OCRBR5101M1L-310 (COMUNIDAD SAN MANUEL)RIO BRAVONUEVO LEONLINARESCITRICOLA SURPOZO-99.5419124.760362020.0231.045Alta2350.0Dudosa para riego1545.8000Cultivos con manejo especialLigeramente salobres0.2000Baja752.0960Muy dura e indeseable usos industrial y domestico1.1Potable - Excelente14.615488No apta como FAAP0.01Potable - Excelente0.003Potable - Excelente0.005Potable - Excelente0.0005Potable - Excelente0.005Potable - Excelente0.00150Potable - Excelente0.02500Potable - ExcelenteRojoSINOSISISINOSINOSISISISISISISI
10501064OCRBR5102M1L-305 (EJIDO OJO DE AGUA LAS CRUCESITAS)RIO BRAVONUEVO LEONLINARESCITRICOLA SURPOZO-99.7009924.782802020.0256.000Alta529.0Buena para riego297.0000Excelente para riegoPotable - Dulce0.2000Baja273.0000Potable - Dura1.1Potable - Excelente77.392000No apta como FAAP0.01Potable - Excelente0.003Potable - Excelente0.005Potable - Excelente0.0005Potable - Excelente0.005Potable - Excelente0.00709Potable - Excelente0.07578Potable - ExcelenteRojoSISISISISISISINOSISISISISISISI
10511065OCRBR5105M2HACIENDA MEXIQUITO POZO 01RIO BRAVONUEVO LEONCADEREYTA JIMENEZCITRICOLA NORTEPOZO-99.8224925.551972020.0330.690Alta2600.0Dudosa para riego1873.0000Cultivos con manejo especialLigeramente salobres0.7574Potable - Optima660.2126Muy dura e indeseable usos industrial y domestico620.0Aceptable36.477104No apta como FAAP0.01Potable - Excelente0.003Potable - Excelente0.005Potable - Excelente0.0005Potable - Excelente0.005Potable - Excelente0.02420Potable - Excelente0.21290Potable - ExcelenteRojoSINOSISISINOSINOSISISISISISISI
10521066OCRBR5106M1COMUNIDAD LOS POCITOSRIO BRAVONUEVO LEONGALEANANAVIDAD-POTOSI-RAICESPOZO-100.3268324.801182020.0193.140Alta873.0Permisible para riego690.6667Cultivos sensiblesPotable - Dulce0.7108Potable - Optima406.3680Potable - Dura1.1Potable - Excelente0.020000Potable - Excelente0.01Potable - Excelente0.003Potable - Excelente0.005Potable - Excelente0.0005Potable - Excelente0.005Potable - Excelente0.01200Potable - Excelente0.17860Potable - ExcelenteVerdeSISISISISISISISISISISISISISISI
10531067OCRBR5109M1COMUNIDAD LA REFORMARIO BRAVONUEVO LEONGALEANANAVIDAD-POTOSI-RAICESPOZO-100.7330225.093802020.0263.070Alta817.0Permisible para riego495.0000Excelente para riegoPotable - Dulce0.4002Media362.5440Potable - Dura1.1Potable - Excelente0.811876Potable - Excelente0.01Potable - Excelente0.003Potable - Excelente0.005Potable - Excelente0.0005Potable - Excelente0.005Potable - Excelente0.00150Potable - Excelente0.02500Potable - ExcelenteVerdeSISISISISISISISISISISISISISISI